debug: set explicit timeout on operation.result()

debug: add more stacktrace
debug: hardcode cli call
2026-09-26 22:51:56 +00:00 · 2022-12-17 01:38:07 +00:00 · 2022-12-17 01:10:46 +00:00 · 2022-12-17 00:59:42 +00:00 · 2022-12-17 00:34:53 +00:00 · 2022-12-16 23:45:43 +00:00
152 changed files with 17611 additions and 5969 deletions
@@ -109,3 +109,4 @@ class EndpointResourceCleanupManager(VertexAIResourceCleanupManager):

 class ModelResourceCleanupManager(VertexAIResourceCleanupManager):
    vertex_ai_resource = aiplatform.Model
+
@@ -245,13 +245,15 @@ def process_and_execute_notebook(
        result.logs_bucket = operation_metadata.build.logs_bucket

        # Block and wait for the result
-        operation_result = operation.result()
+        operation_result = operation.result(timeout=84600)

        result.duration = datetime.datetime.now() - time_start
        result.is_pass = True
        print(f"{notebook} PASSED in {format_timedelta(result.duration)}.")
    except Exception as error:
        result.error_message = str(error)
+        import traceback
+        traceback.print_exc()

        if operation and should_get_tail_logs:
            # Extract the logs
@@ -66,6 +66,7 @@ def execute_notebook(

    # Execute notebook
    try:
+        print("DEBUG HERE\n")
        # Execute notebook
        pm.execute_notebook(
            input_path=notebook_source,
@@ -45,6 +45,9 @@ def execute_notebook_remote(
    """Create and execute a single notebook on Google Cloud Build"""
    # Load build steps from YAML

+    print(f"DEBUG TIMEOUT {timeout_in_seconds}\n")
+    
+
    cloudbuild_config = yaml.load(open(CLOUD_BUILD_FILEPATH), Loader=FullLoader)

    substitutions = {
@@ -95,7 +98,14 @@ def execute_notebook_remote(
    if tag:
        build.tags = [tag]

-    operation = client.create_build(project_id=project_id, build=build)
+    try:
+        print("DEBUG: START\n")
+        operation = client.create_build(project_id=project_id, build=build)
+    except Exception as e:
+        import traceback
+        traceback.print_exc()
+    print("DEBUG: FINISH\n")
+    print(operation)
    # Print the in-progress operation
    # print("IN PROGRESS:")
    # print(operation.metadata)
@@ -36,7 +36,7 @@ steps:
    - -c
    - |
      . workspace/env/bin/activate &&
-      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_TEST_PATHS_FILE}" --base_branch "${_FORCED_BASE_BRANCH}" --container_uri ${_PYTHON_IMAGE} --staging_bucket ${_GCS_STAGING_BUCKET}  --artifacts_bucket ${_GCS_STAGING_BUCKET}/executed_notebooks/PR_${_PR_NUMBER}/BUILD_${BUILD_ID} --variable_project_id ${PROJECT_ID} --variable_region ${_GCP_REGION} --variable_service_account ${_GCP_SERVICE_ACCOUNT} --variable_vpc_network "${_GPC_VPC_NETWORK_NAME}" `if [ ! -z "${_PRIVATE_POOL_NAME}" ]; then echo "--private_pool_id ${_PRIVATE_POOL_NAME}"; fi`
+      python3 .cloud-build/execute_changed_notebooks_cli.py --test_paths_file "${_TEST_PATHS_FILE}" --base_branch "${_FORCED_BASE_BRANCH}" --container_uri ${_PYTHON_IMAGE} --staging_bucket ${_GCS_STAGING_BUCKET}  --artifacts_bucket ${_GCS_STAGING_BUCKET}/executed_notebooks/PR_${_PR_NUMBER}/BUILD_${BUILD_ID} --variable_project_id ${PROJECT_ID} --variable_region ${_GCP_REGION} --variable_service_account ${_GCP_SERVICE_ACCOUNT} --variable_vpc_network "${_GPC_VPC_NETWORK_NAME}" --timeout 86400 `if [ ! -z "${_PRIVATE_POOL_NAME}" ]; then echo "--private_pool_id ${_PRIVATE_POOL_NAME}"; fi`
    env:
    - 'IS_TESTING=1'
 timeout: 86400s
@@ -8,6 +8,7 @@ matplotlib
 tabulate
 google-cloud-aiplatform
 google-cloud-storage
-google-cloud-build
+google-cloud-build==3.9.3
+protobuf==4.21.9
 ratemate
-GitPython
+GitPython
@@ -7,3 +7,4 @@
 /pluto_on_workbench @wkharold
 /cpr-examples @samthrasher
 /Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines @Ark-kun
+/pipeline_components @Ark-kun
@@ -2,13 +2,13 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1e2558325f4c708aca75827c8acc13d230ee7e9f/components/pandas/Binarize_column/in_CSV_format/component.yaml")
-train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/cb44b75c9c062fcc40c2b905b2024b4493dbc62b/components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
-upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_classification_logistic_regression_model_using_Scikit_learn_pipeline():
@@ -2,15 +2,15 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1e2558325f4c708aca75827c8acc13d230ee7e9f/components/pandas/Binarize_column/in_CSV_format/component.yaml")
-create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1a2ef3eeb77bc278f33cad0dd29008ea2431e191/components/PyTorch/Create_fully_connected_network/component.yaml")
-train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/d8c4cf5e6403bc65bcf8d606e6baf87e2528a3dc/components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
-create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/46d51383e6554b7f3ab4fd8cf614d8c2b422fb22/components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
-upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_classification_model_using_PyTorch_pipeline():
@@ -2,16 +2,16 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1e2558325f4c708aca75827c8acc13d230ee7e9f/components/pandas/Binarize_column/in_CSV_format/component.yaml")
-split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/daae5a4abaa35e44501818b1534ed7827d7da073/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
-create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/9ca0f9eecf5f896f65b8538bbd809747052617d1/components/tensorflow/Create_fully_connected_network/component.yaml")
-train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c504a4010348c50eaaf6d4337586ccc008f4dcef/components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
-predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/59c759ce6f543184e30db6817d2a703879bc0f39/components/tensorflow/Predict/on_CSV/component.yaml")
-upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_classification_model_using_TensorFlow_pipeline():
@@ -2,15 +2,15 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1e2558325f4c708aca75827c8acc13d230ee7e9f/components/pandas/Binarize_column/in_CSV_format/component.yaml")
-split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/daae5a4abaa35e44501818b1534ed7827d7da073/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
-train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/58d3a47f904f32a64af8403330ba7e2134cae46d/components/XGBoost/Train/component.yaml")
-xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/4694ec97baccf59284c2a1db4aa2250c22291eab/components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_classification_model_using_XGBoost_pipeline():
@@ -2,36 +2,36 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1e2558325f4c708aca75827c8acc13d230ee7e9f/components/pandas/Binarize_column/in_CSV_format/component.yaml")
-split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/daae5a4abaa35e44501818b1534ed7827d7da073/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+binarize_column_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Binarize_column/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")

 # TensorFlow
-create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/9ca0f9eecf5f896f65b8538bbd809747052617d1/components/tensorflow/Create_fully_connected_network/component.yaml")
-train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c504a4010348c50eaaf6d4337586ccc008f4dcef/components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
-predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/59c759ce6f543184e30db6817d2a703879bc0f39/components/tensorflow/Predict/on_CSV/component.yaml")
-upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")

 # PyTorch
-create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1a2ef3eeb77bc278f33cad0dd29008ea2431e191/components/PyTorch/Create_fully_connected_network/component.yaml")
-train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/d8c4cf5e6403bc65bcf8d606e6baf87e2528a3dc/components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
-create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/46d51383e6554b7f3ab4fd8cf614d8c2b422fb22/components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
-upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/workaround_for_buggy_KFPv2_compiler/component.yaml")
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")

 # XGBoost
-train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/58d3a47f904f32a64af8403330ba7e2134cae46d/components/XGBoost/Train/component.yaml")
-xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/4694ec97baccf59284c2a1db4aa2250c22291eab/components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")

 # Scikit-learn
-#train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/f807e02b54d4886c65a05f40848fd51c72407f40/components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
-train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/cb44b75c9c062fcc40c2b905b2024b4493dbc62b/components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
-upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
+#train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
+train_logistic_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")

 # Vertex AI
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_classification_model_using_all_frameworks_pipeline():
@@ -2,12 +2,12 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/f807e02b54d4886c65a05f40848fd51c72407f40/components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
-upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_regression_linear_model_using_Scikit_learn_pipeline():
@@ -2,14 +2,14 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1a2ef3eeb77bc278f33cad0dd29008ea2431e191/components/PyTorch/Create_fully_connected_network/component.yaml")
-train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/d8c4cf5e6403bc65bcf8d606e6baf87e2528a3dc/components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
-create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/46d51383e6554b7f3ab4fd8cf614d8c2b422fb22/components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
-upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_regression_model_using_PyTorch_pipeline():
@@ -2,15 +2,15 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/daae5a4abaa35e44501818b1534ed7827d7da073/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
-create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/9ca0f9eecf5f896f65b8538bbd809747052617d1/components/tensorflow/Create_fully_connected_network/component.yaml")
-train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c504a4010348c50eaaf6d4337586ccc008f4dcef/components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
-predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/59c759ce6f543184e30db6817d2a703879bc0f39/components/tensorflow/Predict/on_CSV/component.yaml")
-upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_regression_model_using_Tensorflow_pipeline():
@@ -2,14 +2,14 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/daae5a4abaa35e44501818b1534ed7827d7da073/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
-train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/58d3a47f904f32a64af8403330ba7e2134cae46d/components/XGBoost/Train/component.yaml")
-xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/4694ec97baccf59284c2a1db4aa2250c22291eab/components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_regression_model_using_XGBoost_pipeline():
@@ -2,34 +2,34 @@
 from kfp import components

 # %% Loading components
-download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml")
-select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/8c78aae096806cff3bc331a40566f42f5c3e9d4b/components/pandas/Select_columns/in_CSV_format/component.yaml")
-fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/23405971f5f16a41b16c343129b893c52e4d1d48/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
-split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/daae5a4abaa35e44501818b1534ed7827d7da073/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")
+download_from_gcs_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/storage/download/component.yaml")
+select_columns_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Select_columns/in_CSV_format/component.yaml")
+fill_all_missing_values_using_Pandas_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml")
+split_rows_into_subsets_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml")

 # TensorFlow
-create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/9ca0f9eecf5f896f65b8538bbd809747052617d1/components/tensorflow/Create_fully_connected_network/component.yaml")
-train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c504a4010348c50eaaf6d4337586ccc008f4dcef/components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
-predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/59c759ce6f543184e30db6817d2a703879bc0f39/components/tensorflow/Predict/on_CSV/component.yaml")
-upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
+create_fully_connected_tensorflow_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Create_fully_connected_network/component.yaml")
+train_model_using_Keras_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml")
+predict_with_TensorFlow_model_on_CSV_data_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/tensorflow/Predict/on_CSV/component.yaml")
+upload_Tensorflow_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/component.yaml")

 # PyTorch
-create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/1a2ef3eeb77bc278f33cad0dd29008ea2431e191/components/PyTorch/Create_fully_connected_network/component.yaml")
-train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/d8c4cf5e6403bc65bcf8d606e6baf87e2528a3dc/components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
-create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/46d51383e6554b7f3ab4fd8cf614d8c2b422fb22/components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
-upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/workaround_for_buggy_KFPv2_compiler/component.yaml")
+create_fully_connected_pytorch_network_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_fully_connected_network/component.yaml")
+train_pytorch_model_from_csv_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml")
+create_pytorch_model_archive_with_base_handler_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml")
+upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/component.yaml")

 # XGBoost
-train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/58d3a47f904f32a64af8403330ba7e2134cae46d/components/XGBoost/Train/component.yaml")
-xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/4694ec97baccf59284c2a1db4aa2250c22291eab/components/XGBoost/Predict/component.yaml")
-upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
+train_XGBoost_model_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Train/component.yaml")
+xgboost_predict_on_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/XGBoost/Predict/component.yaml")
+upload_XGBoost_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/component.yaml")

 # Scikit-learn
-train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/f807e02b54d4886c65a05f40848fd51c72407f40/components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
-upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/c6a8b67d1ada2cc17665c99ff6b410df588bee28/components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/workaround_for_buggy_KFPv2_compiler/component.yaml")
+train_linear_regression_model_using_scikit_learn_from_CSV_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/1f5cf6e06409b704064b2086c0a705e4e6b4fcde/community-content/pipeline_components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml")
+upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/component.yaml")

 # Vertex AI
-deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/Ark-kun/pipeline_components/27a5ea25e849c9e8c0cb6ed65518bc3ece259aaf/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml")
+deploy_model_to_endpoint_op = components.load_component_from_url("https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/399405402d95f4a011e2d2e967c96f8508ba5688/community-content/pipeline_components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/component.yaml")

 # %% Pipeline definition
 def train_tabular_regression_model_using_all_frameworks_pipeline():
@@ -0,0 +1,64 @@
+name: Train linear regression model using scikit learn from CSV
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/ML_frameworks/Scikit_learn/Train_linear_regression_model/from_CSV/component.yaml'}
+inputs:
+- {name: dataset, type: CSV}
+- {name: label_column_name, type: String}
+outputs:
+- {name: model, type: ScikitLearnPickleModel}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'scikit-learn==1.0.2' 'pandas==1.4.3' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'scikit-learn==1.0.2' 'pandas==1.4.3'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def train_linear_regression_model_using_scikit_learn_from_CSV(
+          dataset_path,
+          model_path,
+          label_column_name,
+      ):
+          import pandas
+          import pickle
+          from sklearn import linear_model
+
+          df = pandas.read_csv(dataset_path)
+          model = linear_model.LinearRegression()
+          model.fit(
+              X=df.drop(columns=label_column_name),
+              y=df[label_column_name],
+          )
+
+          with open(model_path, "wb") as f:
+              pickle.dump(model, f)
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Train linear regression model using scikit learn from CSV', description='')
+      _parser.add_argument("--dataset", dest="dataset_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = train_linear_regression_model_using_scikit_learn_from_CSV(**_parsed_args)
+    args:
+    - --dataset
+    - {inputPath: dataset}
+    - --label-column-name
+    - {inputValue: label_column_name}
+    - --model
+    - {outputPath: model}
@@ -0,0 +1,163 @@
+name: Train logistic regression model using scikit learn from CSV
+description: Train logistic regression model using Scikit-learn
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/ML_frameworks/Scikit_learn/Train_logistic_regression_model/from_CSV/component.yaml'}
+inputs:
+- {name: dataset, type: CSV}
+- {name: label_column_name, type: String}
+- {name: penalty, type: String, default: l2, optional: true}
+- {name: solver, type: String, default: lbfgs, optional: true}
+- {name: max_iterations, type: Integer, default: '100', optional: true}
+- {name: multi_class_mode, type: String, default: auto, optional: true}
+- {name: random_seed, type: Integer, default: '0', optional: true}
+outputs:
+- {name: model, type: ScikitLearnPickleModel}
+- {name: model_parameters, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'scikit-learn==1.0.2' 'pandas==1.4.3' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'scikit-learn==1.0.2' 'pandas==1.4.3'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def train_logistic_regression_model_using_scikit_learn_from_CSV(
+          dataset_path,
+          model_path,
+          label_column_name,
+          penalty = "l2", # l1, l2, elasticnet, none
+          solver = "lbfgs", # newton-cg, lbfgs, liblinear, sag, saga
+          max_iterations = 100,
+          multi_class_mode = "auto", # auto, ovr, multinomial
+          random_seed = 0,
+      ):
+          """Train logistic regression model using Scikit-learn
+
+          See https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
+          """
+          import json
+          import pandas
+          import pickle
+          from sklearn import linear_model
+
+          df = pandas.read_csv(dataset_path)
+          model = linear_model.LogisticRegression(
+              penalty=penalty,
+              #dual=False,
+              #tol=1e-4,
+              #C=1.0,
+              #fit_intercept=True,
+              #intercept_scaling=1,
+              #class_weight=None,
+              random_state=random_seed,
+              solver=solver,
+              max_iter=max_iterations,
+              multi_class=multi_class_mode,
+              #l1_ratio=None,
+              verbose=1,
+          )
+
+          model_parameters = model.get_params()
+          model_parameters_json = json.dumps(model_parameters, indent=2)
+          print("Model parameters:")
+          print(model_parameters_json)
+          print()
+
+          model.fit(
+              X=df.drop(columns=label_column_name),
+              y=df[label_column_name],
+          )
+
+          with open(model_path, "wb") as f:
+              pickle.dump(model, f)
+
+          return (model_parameters_json,)
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Train logistic regression model using scikit learn from CSV', description='Train logistic regression model using Scikit-learn')
+      _parser.add_argument("--dataset", dest="dataset_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--penalty", dest="penalty", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--solver", dest="solver", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--max-iterations", dest="max_iterations", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--multi-class-mode", dest="multi_class_mode", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--random-seed", dest="random_seed", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=1)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = train_logistic_regression_model_using_scikit_learn_from_CSV(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --dataset
+    - {inputPath: dataset}
+    - --label-column-name
+    - {inputValue: label_column_name}
+    - if:
+        cond: {isPresent: penalty}
+        then:
+        - --penalty
+        - {inputValue: penalty}
+    - if:
+        cond: {isPresent: solver}
+        then:
+        - --solver
+        - {inputValue: solver}
+    - if:
+        cond: {isPresent: max_iterations}
+        then:
+        - --max-iterations
+        - {inputValue: max_iterations}
+    - if:
+        cond: {isPresent: multi_class_mode}
+        then:
+        - --multi-class-mode
+        - {inputValue: multi_class_mode}
+    - if:
+        cond: {isPresent: random_seed}
+        then:
+        - --random-seed
+        - {inputValue: random_seed}
+    - --model
+    - {outputPath: model}
+    - '----output-paths'
+    - {outputPath: model_parameters}
@@ -0,0 +1,41 @@
+name: Create PyTorch Model Archive with base handler
+inputs:
+- {name: Model, type: PyTorchScriptModule}
+- {name: Model name, type: String, default: model}
+- {name: Model version, type: String, default: "1.0"}
+outputs:
+- {name: Model archive, type: PyTorchModelArchive}
+metadata:
+  annotations:
+    author: Alexey Volkov <alexey.volkov@ark-kun.com>
+    canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/PyTorch/Create_PyTorch_Model_Archive/with_base_handler/component.yaml'
+implementation:
+  container:
+    image: pytorch/torchserve:0.6.0-cpu
+    command:
+    - bash
+    - -exc
+    - |
+      model_path=$0
+      model_name=$1
+      model_version=$2
+      output_model_archive_path=$3
+
+      mkdir -p "$(dirname "$output_model_archive_path")"
+
+      # TODO: Use the built-in base_handler once my fix is merged: https://github.com/pytorch/serve/pull/1682
+      echo '
+      from ts.torch_handler import base_handler
+      class BaseHandler(base_handler.BaseHandler):
+          pass
+      ' > base_handler.py  # torch-model-archiver needs the handler to have .py extension
+      torch-model-archiver --model-name "$model_name" --version "$model_version" --serialized-file "$model_path" --handler base_handler.py
+
+      # torch-model-archiver does not allow specifying the output path, but always writes to "${model_name}.<format>"
+      expected_model_archive_path="${model_name}.mar"
+      mv "$expected_model_archive_path" "$output_model_archive_path"
+
+    - {inputPath: Model}
+    - {inputValue: Model name}
+    - {inputValue: Model version}
+    - {outputPath: Model archive}
@@ -0,0 +1,117 @@
+name: Create fully connected pytorch network
+description: Creates fully-connected network in PyTorch ScriptModule format
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/PyTorch/Create_fully_connected_network/component.yaml'}
+inputs:
+- {name: input_size, type: Integer}
+- {name: hidden_layer_sizes, type: JsonArray, default: '[]', optional: true}
+- {name: output_size, type: Integer, default: '1', optional: true}
+- {name: activation_name, type: String, default: relu, optional: true}
+- {name: output_activation_name, type: String, optional: true}
+- {name: random_seed, type: Integer, default: '0', optional: true}
+outputs:
+- {name: model, type: PyTorchScriptModule}
+implementation:
+  container:
+    image: pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime
+    command:
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def create_fully_connected_pytorch_network(
+          input_size,
+          model_path,
+          hidden_layer_sizes = [],
+          output_size = 1,
+          activation_name = 'relu',
+          output_activation_name = None,
+          random_seed = 0,
+      ):
+          '''Creates fully-connected network in PyTorch ScriptModule format'''
+          import torch
+          torch.manual_seed(random_seed)
+
+          activation = getattr(torch, activation_name, None) or getattr(torch.nn.functional, activation_name, None)
+          if not activation:
+              raise ValueError(f'Activation "{activation_name}" was not found.')
+
+          class ActivationLayer(torch.nn.Module):
+              def forward(self, input):
+                  return activation(input)
+
+          layers = []
+          prev_layer_size = input_size
+          for layer_size in hidden_layer_sizes:
+              layer = torch.nn.Linear(prev_layer_size, layer_size)
+              prev_layer_size = layer_size
+              layers.append(layer)
+              layers.append(ActivationLayer())
+
+          # Adding the output layer
+          layers.append(torch.nn.Linear(prev_layer_size, output_size))
+
+          # Adding the optional activation after the output layer
+          if output_activation_name:
+              output_activation = getattr(torch, output_activation_name, None) or getattr(torch.nn.functional, output_activation_name, None)
+              class OutputActivationLayer(torch.nn.Module):
+                  def forward(self, input):
+                      return output_activation(input)
+              layers.append(OutputActivationLayer())
+
+          network = torch.nn.Sequential(*layers)
+          script_module = torch.jit.script(network)
+          print(script_module)
+          script_module.save(model_path)
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Create fully connected pytorch network', description='Creates fully-connected network in PyTorch ScriptModule format')
+      _parser.add_argument("--input-size", dest="input_size", type=int, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--hidden-layer-sizes", dest="hidden_layer_sizes", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--output-size", dest="output_size", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--activation-name", dest="activation_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--output-activation-name", dest="output_activation_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--random-seed", dest="random_seed", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = create_fully_connected_pytorch_network(**_parsed_args)
+    args:
+    - --input-size
+    - {inputValue: input_size}
+    - if:
+        cond: {isPresent: hidden_layer_sizes}
+        then:
+        - --hidden-layer-sizes
+        - {inputValue: hidden_layer_sizes}
+    - if:
+        cond: {isPresent: output_size}
+        then:
+        - --output-size
+        - {inputValue: output_size}
+    - if:
+        cond: {isPresent: activation_name}
+        then:
+        - --activation-name
+        - {inputValue: activation_name}
+    - if:
+        cond: {isPresent: output_activation_name}
+        then:
+        - --output-activation-name
+        - {inputValue: output_activation_name}
+    - if:
+        cond: {isPresent: random_seed}
+        then:
+        - --random-seed
+        - {inputValue: random_seed}
+    - --model
+    - {outputPath: model}
@@ -0,0 +1,209 @@
+name: Train pytorch model from csv
+description: Trains PyTorch model
+metadata:
+  annotations:
+    author: Alexey Volkov <alexey.volkov@ark-kun.com>
+    canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/PyTorch/Train_PyTorch_model/from_CSV/component.yaml'
+inputs:
+- {name: model, type: PyTorchScriptModule}
+- {name: training_data, type: CSV}
+- {name: label_column_name, type: String}
+- {name: loss_function_name, type: String, default: mse_loss, optional: true}
+- {name: number_of_epochs, type: Integer, default: '1', optional: true}
+- {name: learning_rate, type: Float, default: '0.1', optional: true}
+- {name: optimizer_name, type: String, default: Adadelta, optional: true}
+- {name: optimizer_parameters, type: JsonObject, optional: true}
+- {name: batch_size, type: Integer, default: '32', optional: true}
+- {name: batch_log_interval, type: Integer, default: '100', optional: true}
+- {name: random_seed, type: Integer, default: '0', optional: true}
+outputs:
+- {name: trained_model, type: PyTorchScriptModule}
+implementation:
+  container:
+    image: pytorch/pytorch:1.7.1-cuda11.0-cudnn8-runtime
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'pandas==1.4.3' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet
+      --no-warn-script-location 'pandas==1.4.3' --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def train_pytorch_model_from_csv(
+          model_path,
+          training_data_path,
+          trained_model_path,
+          label_column_name,
+          loss_function_name = 'mse_loss',
+          number_of_epochs = 1,
+          learning_rate = 0.1,
+          optimizer_name = 'Adadelta',
+          optimizer_parameters = None,
+          batch_size = 32,
+          batch_log_interval = 100,
+          random_seed = 0,
+      ):
+          '''Trains PyTorch model'''
+          import pandas
+          import torch
+
+          torch.manual_seed(random_seed)
+
+          use_cuda = torch.cuda.is_available()
+          device = torch.device("cuda" if use_cuda else "cpu")
+
+          model = torch.jit.load(model_path)
+          model.to(device)
+          model.train()
+
+          optimizer_class = getattr(torch.optim, optimizer_name, None)
+          if not optimizer_class:
+              raise ValueError(f'Optimizer "{optimizer_name}" was not found.')
+
+          optimizer_parameters = optimizer_parameters or {}
+          optimizer_parameters['lr'] = learning_rate
+          optimizer = optimizer_class(model.parameters(), **optimizer_parameters)
+
+          loss_function = getattr(torch, loss_function_name, None) or getattr(torch.nn, loss_function_name, None) or getattr(torch.nn.functional, loss_function_name, None)
+          if not loss_function:
+              raise ValueError(f'Loss function "{loss_function_name}" was not found.')
+
+          class CsvDataset(torch.utils.data.Dataset):
+
+              def __init__(self, file_path, label_column_name, drop_nan_columns_or_rows = 'columns'):
+                  dataframe = pandas.read_csv(file_path).convert_dtypes()
+                  # Preventing error: default_collate: batch must contain tensors, numpy arrays, numbers, dicts or lists; found object
+                  if drop_nan_columns_or_rows == 'columns':
+                      non_nan_data = dataframe.dropna(axis='columns')
+                      removed_columns = set(dataframe.columns) - set(non_nan_data.columns)
+                      if removed_columns:
+                          print('Skipping columns with NaNs: ' + str(removed_columns))
+                      dataframe = non_nan_data
+                  if drop_nan_columns_or_rows == 'rows':
+                      non_nan_data = dataframe.dropna(axis='index')
+                      number_of_removed_rows = len(dataframe) - len(non_nan_data)
+                      if number_of_removed_rows:
+                          print(f'Skipped {number_of_removed_rows} rows with NaNs.')
+                      dataframe = non_nan_data
+                  numerical_data = dataframe.select_dtypes(include='number')
+                  non_numerical_data = dataframe.select_dtypes(exclude='number')
+                  if not non_numerical_data.empty:
+                      print('Skipping non-number columns:')
+                      print(non_numerical_data.dtypes)
+                  self._dataframe = dataframe
+                  self.labels = numerical_data[[label_column_name]]
+                  self.features = numerical_data.drop(columns=[label_column_name])
+
+              def __len__(self):
+                  return len(self._dataframe)
+
+              def __getitem__(self, index):
+                  return [self.features.loc[index].to_numpy(dtype='float32'), self.labels.loc[index].to_numpy(dtype='float32')]
+
+          dataset = CsvDataset(
+              file_path=training_data_path,
+              label_column_name=label_column_name,
+          )
+          train_loader = torch.utils.data.DataLoader(
+              dataset=dataset,
+              batch_size=batch_size,
+              shuffle=True,
+          )
+
+          last_full_batch_loss = None
+          for epoch in range(1, number_of_epochs + 1):
+              for batch_idx, (data, target) in enumerate(train_loader):
+                  data, target = data.to(device), target.to(device)
+                  optimizer.zero_grad()
+                  output = model(data)
+                  loss = loss_function(output, target)
+                  loss.backward()
+                  optimizer.step()
+                  if len(data) == batch_size:
+                      last_full_batch_loss = loss.item()
+                  if batch_idx % batch_log_interval == 0:
+                      print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                          epoch, batch_idx * len(data), len(train_loader.dataset),
+                          100. * batch_idx / len(train_loader), loss.item()))
+              print(f'Training epoch {epoch} completed. Last full batch loss: {last_full_batch_loss:.6f}')
+
+          # print(optimizer.state_dict())
+          model.save(trained_model_path)
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Train pytorch model from csv', description='Trains PyTorch model')
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--training-data", dest="training_data_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--loss-function-name", dest="loss_function_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--number-of-epochs", dest="number_of_epochs", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--learning-rate", dest="learning_rate", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimizer-name", dest="optimizer_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimizer-parameters", dest="optimizer_parameters", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--batch-size", dest="batch_size", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--batch-log-interval", dest="batch_log_interval", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--random-seed", dest="random_seed", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--trained-model", dest="trained_model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = train_pytorch_model_from_csv(**_parsed_args)
+    args:
+    - --model
+    - {inputPath: model}
+    - --training-data
+    - {inputPath: training_data}
+    - --label-column-name
+    - {inputValue: label_column_name}
+    - if:
+        cond: {isPresent: loss_function_name}
+        then:
+        - --loss-function-name
+        - {inputValue: loss_function_name}
+    - if:
+        cond: {isPresent: number_of_epochs}
+        then:
+        - --number-of-epochs
+        - {inputValue: number_of_epochs}
+    - if:
+        cond: {isPresent: learning_rate}
+        then:
+        - --learning-rate
+        - {inputValue: learning_rate}
+    - if:
+        cond: {isPresent: optimizer_name}
+        then:
+        - --optimizer-name
+        - {inputValue: optimizer_name}
+    - if:
+        cond: {isPresent: optimizer_parameters}
+        then:
+        - --optimizer-parameters
+        - {inputValue: optimizer_parameters}
+    - if:
+        cond: {isPresent: batch_size}
+        then:
+        - --batch-size
+        - {inputValue: batch_size}
+    - if:
+        cond: {isPresent: batch_log_interval}
+        then:
+        - --batch-log-interval
+        - {inputValue: batch_log_interval}
+    - if:
+        cond: {isPresent: random_seed}
+        then:
+        - --random-seed
+        - {inputValue: random_seed}
+    - --trained-model
+    - {outputPath: trained_model}
@@ -0,0 +1,110 @@
+name: Xgboost predict on CSV
+description: Makes predictions using a trained XGBoost model.
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/XGBoost/Predict/component.yaml'}
+inputs:
+- {name: data, type: CSV, description: Feature data in Apache Parquet format.}
+- {name: model, type: XGBoostModel, description: Trained model in binary XGBoost format.}
+- {name: label_column_name, type: String, description: Optional. Name of the column
+    containing the label data that is excluded during the prediction., optional: true}
+outputs:
+- {name: predictions, description: Model predictions.}
+implementation:
+  container:
+    image: python:3.10
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'xgboost==1.6.1' 'pandas==1.4.3' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'xgboost==1.6.1' 'pandas==1.4.3'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def xgboost_predict_on_CSV(
+          data_path,
+          model_path,
+          predictions_path,
+          label_column_name = None,
+      ):
+          """Makes predictions using a trained XGBoost model.
+
+          Args:
+              data_path: Feature data in Apache Parquet format.
+              model_path: Trained model in binary XGBoost format.
+              predictions_path: Model predictions.
+              label_column_name: Optional. Name of the column containing the label data that is excluded during the prediction.
+
+          Annotations:
+              author: Alexey Volkov <alexey.volkov@ark-kun.com>
+          """
+          from pathlib import Path
+
+          import numpy
+          import pandas
+          import xgboost
+
+          df = pandas.read_csv(
+              data_path,
+          ).convert_dtypes()
+          print("Evaluation data information:")
+          df.info(verbose=True)
+          # Converting column types that XGBoost does not support
+          for column_name, dtype in df.dtypes.items():
+              if dtype in ["string", "object"]:
+                  print(f"Treating the {dtype.name} column '{column_name}' as categorical.")
+                  df[column_name] = df[column_name].astype("category")
+                  print(f"Inferred {len(df[column_name].cat.categories)} categories for the '{column_name}' column.")
+              # Working around the XGBoost issue with nullable floats: https://github.com/dmlc/xgboost/issues/8213
+              if pandas.api.types.is_float_dtype(dtype):
+                  # Converting from "Float64" to "float64"
+                  df[column_name] = df[column_name].astype(dtype.name.lower())
+          print("Final evaluation data information:")
+          df.info(verbose=True)
+
+          if label_column_name is not None:
+              df = df.drop(columns=[label_column_name])
+
+          testing_data = xgboost.DMatrix(
+              data=df,
+              enable_categorical=True,
+          )
+
+          model = xgboost.Booster(model_file=model_path)
+
+          predictions = model.predict(testing_data)
+
+          Path(predictions_path).parent.mkdir(parents=True, exist_ok=True)
+          numpy.savetxt(predictions_path, predictions)
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Xgboost predict on CSV', description='Makes predictions using a trained XGBoost model.')
+      _parser.add_argument("--data", dest="data_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--predictions", dest="predictions_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = xgboost_predict_on_CSV(**_parsed_args)
+    args:
+    - --data
+    - {inputPath: data}
+    - --model
+    - {inputPath: model}
+    - if:
+        cond: {isPresent: label_column_name}
+        then:
+        - --label-column-name
+        - {inputValue: label_column_name}
+    - --predictions
+    - {outputPath: predictions}
@@ -0,0 +1,241 @@
+name: Train XGBoost model on CSV
+description: Trains an XGBoost model.
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/XGBoost/Train/component.yaml'}
+inputs:
+- {name: training_data, type: CSV, description: Training data in CSV format.}
+- {name: label_column_name, type: String, description: Name of the column containing
+    the label data.}
+- {name: starting_model, type: XGBoostModel, description: Existing trained model to
+    start from (in the binary XGBoost format)., optional: true}
+- {name: num_iterations, type: Integer, description: Number of boosting iterations.,
+  default: '10', optional: true}
+- name: objective
+  type: String
+  description: |-
+    The learning task and the corresponding learning objective.
+    See https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters
+    The most common values are:
+    "reg:squarederror" - Regression with squared loss (default).
+    "reg:logistic" - Logistic regression.
+    "binary:logistic" - Logistic regression for binary classification, output probability.
+    "binary:logitraw" - Logistic regression for binary classification, output score before logistic transformation
+    "rank:pairwise" - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
+    "rank:ndcg" - Use LambdaMART to perform list-wise ranking where Normalized Discounted Cumulative Gain (NDCG) is maximized
+  default: reg:squarederror
+  optional: true
+- {name: booster, type: String, description: 'The booster to use. Can be `gbtree`,
+    `gblinear` or `dart`; `gbtree` and `dart` use tree based models while `gblinear`
+    uses linear functions.', default: gbtree, optional: true}
+- {name: learning_rate, type: Float, description: 'Step size shrinkage used in update
+    to prevents overfitting. Range: [0,1].', default: '0.3', optional: true}
+- name: min_split_loss
+  type: Float
+  description: |-
+    Minimum loss reduction required to make a further partition on a leaf node of the tree.
+    The larger `min_split_loss` is, the more conservative the algorithm will be. Range: [0,Inf].
+  default: '0'
+  optional: true
+- name: max_depth
+  type: Integer
+  description: |-
+    Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.
+    0 indicates no limit on depth. Range: [0,Inf].
+  default: '6'
+  optional: true
+- {name: booster_params, type: JsonObject, description: 'Parameters for the booster.
+    See https://xgboost.readthedocs.io/en/latest/parameter.html', optional: true}
+outputs:
+- {name: model, type: XGBoostModel, description: Trained model in the binary XGBoost
+    format.}
+- {name: model_config, type: XGBoostModelConfig, description: The internal parameter
+    configuration of Booster as a JSON string.}
+implementation:
+  container:
+    image: python:3.10
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'xgboost==1.6.1' 'pandas==1.4.3' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'xgboost==1.6.1' 'pandas==1.4.3'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def train_XGBoost_model_on_CSV(
+          training_data_path,
+          model_path,
+          model_config_path,
+          label_column_name,
+          starting_model_path = None,
+          num_iterations = 10,
+          # Booster parameters
+          objective = "reg:squarederror",
+          booster = "gbtree",
+          learning_rate = 0.3,
+          min_split_loss = 0,
+          max_depth = 6,
+          booster_params = None,
+      ):
+          """Trains an XGBoost model.
+
+          Args:
+              training_data_path: Training data in CSV format.
+              model_path: Trained model in the binary XGBoost format.
+              model_config_path: The internal parameter configuration of Booster as a JSON string.
+              starting_model_path: Existing trained model to start from (in the binary XGBoost format).
+              label_column_name: Name of the column containing the label data.
+              num_iterations: Number of boosting iterations.
+              booster_params: Parameters for the booster. See https://xgboost.readthedocs.io/en/latest/parameter.html
+              objective: The learning task and the corresponding learning objective.
+                  See https://xgboost.readthedocs.io/en/latest/parameter.html#learning-task-parameters
+                  The most common values are:
+                  "reg:squarederror" - Regression with squared loss (default).
+                  "reg:logistic" - Logistic regression.
+                  "binary:logistic" - Logistic regression for binary classification, output probability.
+                  "binary:logitraw" - Logistic regression for binary classification, output score before logistic transformation
+                  "rank:pairwise" - Use LambdaMART to perform pairwise ranking where the pairwise loss is minimized
+                  "rank:ndcg" - Use LambdaMART to perform list-wise ranking where Normalized Discounted Cumulative Gain (NDCG) is maximized
+              booster: The booster to use. Can be `gbtree`, `gblinear` or `dart`; `gbtree` and `dart` use tree based models while `gblinear` uses linear functions.
+              learning_rate: Step size shrinkage used in update to prevents overfitting. Range: [0,1].
+              min_split_loss: Minimum loss reduction required to make a further partition on a leaf node of the tree.
+                  The larger `min_split_loss` is, the more conservative the algorithm will be. Range: [0,Inf].
+              max_depth: Maximum depth of a tree. Increasing this value will make the model more complex and more likely to overfit.
+                  0 indicates no limit on depth. Range: [0,Inf].
+
+          Annotations:
+              author: Alexey Volkov <alexey.volkov@ark-kun.com>
+          """
+          import pandas
+          import xgboost
+
+          df = pandas.read_csv(
+              training_data_path,
+          ).convert_dtypes()
+          print("Training data information:")
+          df.info(verbose=True)
+          # Converting column types that XGBoost does not support
+          for column_name, dtype in df.dtypes.items():
+              if dtype in ["string", "object"]:
+                  print(f"Treating the {dtype.name} column '{column_name}' as categorical.")
+                  df[column_name] = df[column_name].astype("category")
+                  print(f"Inferred {len(df[column_name].cat.categories)} categories for the '{column_name}' column.")
+              # Working around the XGBoost issue with nullable floats: https://github.com/dmlc/xgboost/issues/8213
+              if pandas.api.types.is_float_dtype(dtype):
+                  # Converting from "Float64" to "float64"
+                  df[column_name] = df[column_name].astype(dtype.name.lower())
+          print()
+          print("Final training data information:")
+          df.info(verbose=True)
+
+          training_data = xgboost.DMatrix(
+              data=df.drop(columns=[label_column_name]),
+              label=df[[label_column_name]],
+              enable_categorical=True,
+          )
+
+          booster_params = booster_params or {}
+          booster_params.setdefault("objective", objective)
+          booster_params.setdefault("booster", booster)
+          booster_params.setdefault("learning_rate", learning_rate)
+          booster_params.setdefault("min_split_loss", min_split_loss)
+          booster_params.setdefault("max_depth", max_depth)
+
+          starting_model = None
+          if starting_model_path:
+              starting_model = xgboost.Booster(model_file=starting_model_path)
+
+          print()
+          print("Training the model:")
+          model = xgboost.train(
+              params=booster_params,
+              dtrain=training_data,
+              num_boost_round=num_iterations,
+              xgb_model=starting_model,
+              evals=[(training_data, "training_data")],
+          )
+
+          # Saving the model in binary format
+          model.save_model(model_path)
+
+          model_config_str = model.save_config()
+          with open(model_config_path, "w") as model_config_file:
+              model_config_file.write(model_config_str)
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Train XGBoost model on CSV', description='Trains an XGBoost model.')
+      _parser.add_argument("--training-data", dest="training_data_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--starting-model", dest="starting_model_path", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--num-iterations", dest="num_iterations", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--objective", dest="objective", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--booster", dest="booster", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--learning-rate", dest="learning_rate", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--min-split-loss", dest="min_split_loss", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--max-depth", dest="max_depth", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--booster-params", dest="booster_params", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--model-config", dest="model_config_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = train_XGBoost_model_on_CSV(**_parsed_args)
+    args:
+    - --training-data
+    - {inputPath: training_data}
+    - --label-column-name
+    - {inputValue: label_column_name}
+    - if:
+        cond: {isPresent: starting_model}
+        then:
+        - --starting-model
+        - {inputPath: starting_model}
+    - if:
+        cond: {isPresent: num_iterations}
+        then:
+        - --num-iterations
+        - {inputValue: num_iterations}
+    - if:
+        cond: {isPresent: objective}
+        then:
+        - --objective
+        - {inputValue: objective}
+    - if:
+        cond: {isPresent: booster}
+        then:
+        - --booster
+        - {inputValue: booster}
+    - if:
+        cond: {isPresent: learning_rate}
+        then:
+        - --learning-rate
+        - {inputValue: learning_rate}
+    - if:
+        cond: {isPresent: min_split_loss}
+        then:
+        - --min-split-loss
+        - {inputValue: min_split_loss}
+    - if:
+        cond: {isPresent: max_depth}
+        then:
+        - --max-depth
+        - {inputValue: max_depth}
+    - if:
+        cond: {isPresent: booster_params}
+        then:
+        - --booster-params
+        - {inputValue: booster_params}
+    - --model
+    - {outputPath: model}
+    - --model-config
+    - {outputPath: model_config}
@@ -0,0 +1,204 @@
+name: Split rows into subsets
+description: Splits the data table according to the split fractions.
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/dataset_manipulation/Split_rows_into_subsets/in_CSV/component.yaml'}
+inputs:
+- {name: table, type: CSV}
+- {name: fraction_1, type: Float, description: 'The proportion of the lines to put
+    into the 1st split. Range: [0, 1]'}
+- name: fraction_2
+  type: Float
+  description: |-
+    The proportion of the lines to put into the 2nd split. Range: [0, 1]
+    If fraction_2 is not specified, then fraction_2 = 1 - fraction_1.
+    The remaining lines go to the 3rd split (if any).
+  optional: true
+- {name: random_seed, type: Integer, default: '0', optional: true}
+outputs:
+- {name: split_1, type: CSV}
+- {name: split_2, type: CSV}
+- {name: split_3, type: CSV}
+- {name: split_1_count, type: Integer}
+- {name: split_2_count, type: Integer}
+- {name: split_3_count, type: Integer}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def split_rows_into_subsets(
+          table_path,
+          split_1_path,
+          split_2_path,
+          split_3_path,
+          fraction_1,
+          fraction_2 = None,
+          random_seed = 0,
+      ):
+          """Splits the data table according to the split fractions.
+
+          Args:
+              fraction_1: The proportion of the lines to put into the 1st split. Range: [0, 1]
+              fraction_2: The proportion of the lines to put into the 2nd split. Range: [0, 1]
+                  If fraction_2 is not specified, then fraction_2 = 1 - fraction_1.
+                  The remaining lines go to the 3rd split (if any).
+          """
+          import random
+
+          random.seed(random_seed)
+
+          SHUFFLE_BUFFER_SIZE = 10000
+
+          num_splits = 3
+
+          if fraction_1 < 0 or fraction_1 > 1:
+              raise ValueError("fraction_1 must be in between 0 and 1.")
+
+          if fraction_2 is None:
+              fraction_2 = 1 - fraction_1
+          if fraction_2 < 0 or fraction_2 > 1:
+              raise ValueError("fraction_2 must be in between 0 and 1.")
+
+          fraction_3 = 1 - fraction_1 - fraction_2
+
+          fractions = [
+              fraction_1,
+              fraction_2,
+              fraction_3,
+          ]
+
+          assert sum(fractions) == 1
+
+          written_line_counts = [0] * num_splits
+
+          output_files = [
+              open(split_1_path, "wb"),
+              open(split_2_path, "wb"),
+              open(split_3_path, "wb"),
+          ]
+
+          with open(table_path, "rb") as input_file:
+              # Writing the headers
+              header_line = input_file.readline()
+              for output_file in output_files:
+                  output_file.write(header_line)
+
+              while True:
+                  line_buffer = []
+                  for i in range(SHUFFLE_BUFFER_SIZE):
+                      line = input_file.readline()
+                      if not line:
+                          break
+                      line_buffer.append(line)
+
+                  # We need to exactly partition the lines between the output files
+                  # To overcome possible systematic bias, we could calculate the total numbers
+                  # of lines written to each file and take that into account.
+                  num_read_lines = len(line_buffer)
+                  number_of_lines_for_files = [0] * num_splits
+                  # List that will have the index of the destination file for each line
+                  file_index_for_line = []
+                  remaining_lines = num_read_lines
+                  remaining_fraction = 1
+                  for i in range(num_splits):
+                      number_of_lines_for_file = (
+                          round(remaining_lines * (fractions[i] / remaining_fraction))
+                          if remaining_fraction > 0
+                          else 0
+                      )
+                      number_of_lines_for_files[i] = number_of_lines_for_file
+                      remaining_lines -= number_of_lines_for_file
+                      remaining_fraction -= fractions[i]
+                      file_index_for_line.extend([i] * number_of_lines_for_file)
+
+                  assert remaining_lines == 0, f"{remaining_lines}"
+                  assert len(file_index_for_line) == num_read_lines
+
+                  random.shuffle(file_index_for_line)
+
+                  for i in range(num_read_lines):
+                      output_files[file_index_for_line[i]].write(line_buffer[i])
+                      written_line_counts[file_index_for_line[i]] += 1
+
+                  # Exit if the file ended before we were able to fully fill the buffer
+                  if len(line_buffer) != SHUFFLE_BUFFER_SIZE:
+                      break
+
+          for output_file in output_files:
+              output_file.close()
+
+          return written_line_counts
+
+      def _serialize_int(int_value: int) -> str:
+          if isinstance(int_value, str):
+              return int_value
+          if not isinstance(int_value, int):
+              raise TypeError('Value "{}" has type "{}" instead of int.'.format(str(int_value), str(type(int_value))))
+          return str(int_value)
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Split rows into subsets', description='Splits the data table according to the split fractions.')
+      _parser.add_argument("--table", dest="table_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--fraction-1", dest="fraction_1", type=float, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--fraction-2", dest="fraction_2", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--random-seed", dest="random_seed", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--split-1", dest="split_1_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--split-2", dest="split_2_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--split-3", dest="split_3_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=3)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = split_rows_into_subsets(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_int,
+          _serialize_int,
+          _serialize_int,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --table
+    - {inputPath: table}
+    - --fraction-1
+    - {inputValue: fraction_1}
+    - if:
+        cond: {isPresent: fraction_2}
+        then:
+        - --fraction-2
+        - {inputValue: fraction_2}
+    - if:
+        cond: {isPresent: random_seed}
+        then:
+        - --random-seed
+        - {inputValue: random_seed}
+    - --split-1
+    - {outputPath: split_1}
+    - --split-2
+    - {outputPath: split_2}
+    - --split-3
+    - {outputPath: split_3}
+    - '----output-paths'
+    - {outputPath: split_1_count}
+    - {outputPath: split_2_count}
+    - {outputPath: split_3_count}
@@ -0,0 +1,241 @@
+name: Deploy model to endpoint for Google Cloud Vertex AI Model
+description: Deploys Google Cloud Vertex AI Model to a Google Cloud Vertex AI Endpoint.
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/KFPv2_hell/components/google-cloud/Vertex_AI/Models/Deploy_to_endpoint/workaround_for_buggy_KFPv2_compiler/component.yaml'}
+inputs:
+- {name: model_name, type: String, description: Full resource name of a Google Cloud
+    Vertex AI Model}
+- name: endpoint_name
+  type: String
+  description: |-
+    Optional. Full name of Google Cloud Vertex Endpoint. A new
+    endpoint is created if the name is not passed.
+  optional: true
+- name: machine_type
+  type: String
+  description: |-
+    The type of the machine. See the [list of machine types
+    supported for prediction
+    ](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types).
+    Defaults to "n1-standard-2"
+  default: n1-standard-2
+  optional: true
+- name: min_replica_count
+  type: Integer
+  description: |-
+    Optional. The minimum number of machine replicas this deployed
+    model will be always deployed on. If traffic against it increases,
+    it may dynamically be deployed onto more replicas, and as traffic
+    decreases, some of these extra replicas may be freed.
+  default: '1'
+  optional: true
+- name: max_replica_count
+  type: Integer
+  description: |-
+    Optional. The maximum number of replicas this deployed model may
+    be deployed on when the traffic against it increases. If requested
+    value is too large, the deployment will error, but if deployment
+    succeeds then the ability to scale the model to that many replicas
+    is guaranteed (barring service outages). If traffic against the
+    deployed model increases beyond what its replicas at maximum may
+    handle, a portion of the traffic will be dropped. If this value
+    is not provided, the smaller value of min_replica_count or 1 will
+    be used.
+  default: '1'
+  optional: true
+- name: accelerator_type
+  type: String
+  description: |-
+    Optional. Hardware accelerator type. Must also set accelerator_count if used.
+    One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100,
+    NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4
+  optional: true
+- {name: accelerator_count, type: Integer, description: Optional. The number of accelerators
+    to attach to a worker replica., optional: true}
+outputs:
+- {name: endpoint_name, type: String}
+- {name: endpoint_dict, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'google-cloud-aiplatform==1.7.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'google-cloud-aiplatform==1.7.0'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def deploy_model_to_endpoint_for_Google_Cloud_Vertex_AI_Model(
+          model_name,
+          endpoint_name = None,
+          machine_type = "n1-standard-2",
+          min_replica_count = 1,
+          max_replica_count = 1,
+          accelerator_type = None,
+          accelerator_count = None,
+          #
+          # Uncomment when anyone requests these:
+          # deployed_model_display_name: str = None,
+          # traffic_percentage: int = 0,
+          # traffic_split: dict = None,
+          # service_account: str = None,
+          # explanation_metadata: "google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata" = None,
+          # explanation_parameters: "google.cloud.aiplatform_v1.types.explanation.ExplanationParameters" = None,
+          #
+          # encryption_spec_key_name: str = None,
+      ):
+          """Deploys Google Cloud Vertex AI Model to a Google Cloud Vertex AI Endpoint.
+
+          Args:
+              model_name: Full resource name of a Google Cloud Vertex AI Model
+              endpoint_name: Optional. Full name of Google Cloud Vertex Endpoint. A new
+                  endpoint is created if the name is not passed.
+              machine_type: The type of the machine. See the [list of machine types
+                  supported for prediction
+                  ](https://cloud.google.com/vertex-ai/docs/predictions/configure-compute#machine-types).
+                  Defaults to "n1-standard-2"
+              min_replica_count (int):
+                  Optional. The minimum number of machine replicas this deployed
+                  model will be always deployed on. If traffic against it increases,
+                  it may dynamically be deployed onto more replicas, and as traffic
+                  decreases, some of these extra replicas may be freed.
+              max_replica_count (int):
+                  Optional. The maximum number of replicas this deployed model may
+                  be deployed on when the traffic against it increases. If requested
+                  value is too large, the deployment will error, but if deployment
+                  succeeds then the ability to scale the model to that many replicas
+                  is guaranteed (barring service outages). If traffic against the
+                  deployed model increases beyond what its replicas at maximum may
+                  handle, a portion of the traffic will be dropped. If this value
+                  is not provided, the smaller value of min_replica_count or 1 will
+                  be used.
+              accelerator_type (str):
+                  Optional. Hardware accelerator type. Must also set accelerator_count if used.
+                  One of ACCELERATOR_TYPE_UNSPECIFIED, NVIDIA_TESLA_K80, NVIDIA_TESLA_P100,
+                  NVIDIA_TESLA_V100, NVIDIA_TESLA_P4, NVIDIA_TESLA_T4
+              accelerator_count (int):
+                  Optional. The number of accelerators to attach to a worker replica.
+          """
+          import json
+          from google.cloud import aiplatform
+
+          model = aiplatform.Model(model_name=model_name)
+
+          if endpoint_name:
+              endpoint = aiplatform.Endpoint(endpoint_name=endpoint_name)
+          else:
+              endpoint_display_name = model.display_name[:118] + "_endpoint"
+              endpoint = aiplatform.Endpoint.create(
+                  display_name=endpoint_display_name,
+                  project=model.project,
+                  location=model.location,
+                  # encryption_spec_key_name=encryption_spec_key_name,
+                  labels={"component-source": "github-com-ark-kun-pipeline-components"},
+              )
+
+          endpoint = model.deploy(
+              endpoint=endpoint,
+              # deployed_model_display_name=deployed_model_display_name,
+              machine_type=machine_type,
+              min_replica_count=min_replica_count,
+              max_replica_count=max_replica_count,
+              accelerator_type=accelerator_type,
+              accelerator_count=accelerator_count,
+              # service_account=service_account,
+              # explanation_metadata=explanation_metadata,
+              # explanation_parameters=explanation_parameters,
+              # encryption_spec_key_name=encryption_spec_key_name,
+          )
+
+          endpoint_json = json.dumps(endpoint.to_dict(), indent=2)
+          print(endpoint_json)
+          return (endpoint.resource_name, endpoint_json)
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      def _serialize_str(str_value: str) -> str:
+          if not isinstance(str_value, str):
+              raise TypeError('Value "{}" has type "{}" instead of str.'.format(str(str_value), str(type(str_value))))
+          return str_value
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Deploy model to endpoint for Google Cloud Vertex AI Model', description='Deploys Google Cloud Vertex AI Model to a Google Cloud Vertex AI Endpoint.')
+      _parser.add_argument("--model-name", dest="model_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--endpoint-name", dest="endpoint_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--machine-type", dest="machine_type", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--min-replica-count", dest="min_replica_count", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--max-replica-count", dest="max_replica_count", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--accelerator-type", dest="accelerator_type", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--accelerator-count", dest="accelerator_count", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=2)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = deploy_model_to_endpoint_for_Google_Cloud_Vertex_AI_Model(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_str,
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --model-name
+    - {inputValue: model_name}
+    - if:
+        cond: {isPresent: endpoint_name}
+        then:
+        - --endpoint-name
+        - {inputValue: endpoint_name}
+    - if:
+        cond: {isPresent: machine_type}
+        then:
+        - --machine-type
+        - {inputValue: machine_type}
+    - if:
+        cond: {isPresent: min_replica_count}
+        then:
+        - --min-replica-count
+        - {inputValue: min_replica_count}
+    - if:
+        cond: {isPresent: max_replica_count}
+        then:
+        - --max-replica-count
+        - {inputValue: max_replica_count}
+    - if:
+        cond: {isPresent: accelerator_type}
+        then:
+        - --accelerator-type
+        - {inputValue: accelerator_type}
+    - if:
+        cond: {isPresent: accelerator_count}
+        then:
+        - --accelerator-count
+        - {inputValue: accelerator_count}
+    - '----output-paths'
+    - {outputPath: endpoint_name}
+    - {outputPath: endpoint_dict}
@@ -0,0 +1,297 @@
+name: Upload PyTorch model archive to Google Cloud Vertex AI
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/KFPv2_hell/components/google-cloud/Vertex_AI/Models/Upload_PyTorch_model_archive/workaround_for_buggy_KFPv2_compiler/component.yaml'}
+inputs:
+- {name: model_archive, type: PyTorchModelArchive}
+- {name: torchserve_version, type: String, default: 0.6.0, optional: true}
+- name: use_gpu
+  type: Boolean
+  default: "False"
+  optional: true
+- {name: display_name, type: String, optional: true}
+- {name: description, type: String, optional: true}
+- {name: project, type: String, optional: true}
+- {name: location, type: String, optional: true}
+- {name: labels, type: JsonObject, optional: true}
+- {name: staging_bucket, type: String, optional: true}
+outputs:
+- {name: model_name, type: String}
+- {name: model_dict, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'google-cloud-aiplatform==1.13.1' 'google-cloud-build==3.8.3' || PIP_DISABLE_PIP_VERSION_CHECK=1
+      python3 -m pip install --quiet --no-warn-script-location 'google-cloud-aiplatform==1.13.1'
+      'google-cloud-build==3.8.3' --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI(
+          model_archive_path,
+          torchserve_version = "0.6.0",
+          use_gpu = False,
+
+          display_name = None,
+          description = None,
+
+          # Uncomment when anyone requests these:
+          # instance_schema_uri: str = None,
+          # parameters_schema_uri: str = None,
+          # prediction_schema_uri: str = None,
+          # explanation_metadata: "google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata" = None,
+          # explanation_parameters: "google.cloud.aiplatform_v1.types.explanation.ExplanationParameters" = None,
+
+          project = None,
+          location = None,
+          labels = None,
+          # encryption_spec_key_name: str = None,
+          staging_bucket = None,
+      ):
+          import json
+          import os
+          from google.cloud import aiplatform
+
+          if not location:
+              location = os.environ.get("CLOUD_ML_REGION")
+
+          if not labels:
+              labels = {}
+          labels["component-source"] = "github-com-ark-kun-pipeline-components"
+
+          container_image_tag = torchserve_version + "-" + ("gpu" if use_gpu else "cpu")
+          container_image_uri = f"pytorch/torchserve:{container_image_tag}"
+
+          # Vertex Endpoints refuse to support non-Google container registries.
+          # We have to work around this to reduce user frustration
+          # TODO: Remove this code when Vertex Endpoints service starts supporting other container registries.
+          def copy_container_image(
+              src_container_image_uri,
+              dst_container_image_uri,
+              project_id,
+          ):
+              from google.cloud.devtools import cloudbuild
+              from google import protobuf
+              build_client = cloudbuild.CloudBuildClient()
+              build_config = cloudbuild.Build(
+                  images=[dst_container_image_uri],
+                  steps=[
+                      cloudbuild.BuildStep(
+                          name="gcr.io/cloud-builders/docker",
+                          entrypoint="bash",
+                          args=[
+                              "-exc",
+                              'docker pull --quiet "$0" && docker tag "$0" "$1"',
+                              src_container_image_uri,
+                              dst_container_image_uri,
+                          ],
+                      ),
+                  ],
+                  timeout=protobuf.duration_pb2.Duration(
+                      seconds=1800,
+                  ),
+              )
+              build_operation = build_client.create_build(
+                  project_id=project_id,
+                  build=build_config,
+              )
+              try:
+                  result = build_operation.result()
+              except:
+                  print(f"Logs are available at [{build_operation.metadata.build.log_url}].")
+                  raise
+              return result
+
+          project_id = aiplatform.initializer.global_config.project
+          mirrored_container_uri = f"gcr.io/{project_id}/container_mirror/{container_image_uri}"
+          # FIX: Only mirror when image does not exist
+          # docker does is unable to get the registry data from inside container (it cannot connecto to docker socket):
+          # docker.errors.DockerException: Error while fetching server API version: ('Connection aborted.', FileNotFoundError(2, 'No such file or directory'))
+          # import docker
+          # try:
+          #     docker_client = docker.from_env()
+          #     docker_client.images.get_registry_data(mirrored_container_uri)
+          # except docker.errors.NotFound:
+          if True:
+              print(f"Mirroring {container_image_uri} to {mirrored_container_uri}")
+              copy_container_image(
+                  src_container_image_uri=container_image_uri,
+                  dst_container_image_uri=mirrored_container_uri,
+                  project_id=project_id,
+              )
+          container_image_uri = mirrored_container_uri
+          # End of container image mirroring code
+
+          model_archive_file_name = os.path.basename(model_archive_path)
+          model_archive_dir = os.path.dirname(model_archive_path)
+
+          model = aiplatform.Model.upload(
+              # FIX: Use public image or mirror the official image
+              #serving_container_image_uri="gcr.io/avolkov-31337/mirror/pytorch/torchserve",
+              serving_container_image_uri=container_image_uri,
+              artifact_uri=model_archive_dir,
+              serving_container_command=[
+                  "bash",
+                  "-exc",
+                  '''
+      model_archive_uri="$0"
+      #model_archive_local_path=$(mktemp --suffix ".mar")
+      # For some reason the model must already be inside the model-store directory.
+      model_archive_local_path=./model-store/model.mar
+
+      # Downloading the model archive from GCS
+      # TODO: Fix gsutil bugs (requires project ID, has auth issues) and use gsutil instead.
+      # gsutil cp "$model_archive_uri" "$model_archive_local_path"
+      pip install google-cloud-storage
+      python -c '
+      import sys
+      from google.cloud import storage
+
+      model_archive_uri = sys.argv[1]
+      model_archive_local_path = sys.argv[2]
+
+      storage_client = storage.Client()
+      blob = storage.Blob.from_string(uri=model_archive_uri, client=storage_client)
+      blob.download_to_filename(filename=model_archive_local_path)
+      ' "$model_archive_uri" "$model_archive_local_path"
+
+      #Note: config.properties is owned by root. Our user is not root.
+      echo "
+      service_envelope=json
+      # Needed for external access
+      inference_address=http://0.0.0.0:8080
+      management_address=http://0.0.0.0:8081
+      " > config2.properties
+      torchserve --start --foreground --no-config-snapshots --models main-model="$model_archive_local_path" --model-store ./model-store/ --ts-config config2.properties
+                  ''',
+                  "$(AIP_STORAGE_URI)/" + model_archive_file_name,
+              ],
+              serving_container_predict_route="/predictions/main-model",
+              #serving_container_predict_route="/v1/models/main-model:predict",
+              serving_container_health_route="/ping",
+              serving_container_ports=[8080],
+
+              display_name=display_name,
+              description=description,
+
+              # instance_schema_uri=instance_schema_uri,
+              # parameters_schema_uri=parameters_schema_uri,
+              # prediction_schema_uri=prediction_schema_uri,
+              # explanation_metadata=explanation_metadata,
+              # explanation_parameters=explanation_parameters,
+
+              project=project,
+              location=location,
+              labels=labels,
+              # encryption_spec_key_name=encryption_spec_key_name,
+              staging_bucket=staging_bucket,
+          )
+          model_json = json.dumps(model.to_dict(), indent=2)
+          print(model_json)
+          return (model.resource_name, model_json)
+
+      def _deserialize_bool(s) -> bool:
+          from distutils.util import strtobool
+          return strtobool(s) == 1
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      def _serialize_str(str_value: str) -> str:
+          if not isinstance(str_value, str):
+              raise TypeError('Value "{}" has type "{}" instead of str.'.format(str(str_value), str(type(str_value))))
+          return str_value
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Upload PyTorch model archive to Google Cloud Vertex AI', description='')
+      _parser.add_argument("--model-archive", dest="model_archive_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--torchserve-version", dest="torchserve_version", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--use-gpu", dest="use_gpu", type=_deserialize_bool, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--display-name", dest="display_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--description", dest="description", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--project", dest="project", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--location", dest="location", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--labels", dest="labels", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--staging-bucket", dest="staging_bucket", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=2)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = upload_PyTorch_model_archive_to_Google_Cloud_Vertex_AI(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_str,
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --model-archive
+    - {inputPath: model_archive}
+    - if:
+        cond: {isPresent: torchserve_version}
+        then:
+        - --torchserve-version
+        - {inputValue: torchserve_version}
+    - if:
+        cond: {isPresent: use_gpu}
+        then:
+        - --use-gpu
+        - {inputValue: use_gpu}
+    - if:
+        cond: {isPresent: display_name}
+        then:
+        - --display-name
+        - {inputValue: display_name}
+    - if:
+        cond: {isPresent: description}
+        then:
+        - --description
+        - {inputValue: description}
+    - if:
+        cond: {isPresent: project}
+        then:
+        - --project
+        - {inputValue: project}
+    - if:
+        cond: {isPresent: location}
+        then:
+        - --location
+        - {inputValue: location}
+    - if:
+        cond: {isPresent: labels}
+        then:
+        - --labels
+        - {inputValue: labels}
+    - if:
+        cond: {isPresent: staging_bucket}
+        then:
+        - --staging-bucket
+        - {inputValue: staging_bucket}
+    - '----output-paths'
+    - {outputPath: model_name}
+    - {outputPath: model_dict}
@@ -0,0 +1,181 @@
+name: Upload Scikit learn pickle model to Google Cloud Vertex AI
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/KFPv2_hell/components/google-cloud/Vertex_AI/Models/Upload_Scikit-learn_pickle_model/workaround_for_buggy_KFPv2_compiler/component.yaml'}
+inputs:
+- {name: model, type: ScikitLearnPickleModel}
+- {name: sklearn_version, type: String, optional: true}
+- {name: display_name, type: String, optional: true}
+- {name: description, type: String, optional: true}
+- {name: project, type: String, optional: true}
+- {name: location, type: String, optional: true}
+- {name: labels, type: JsonObject, optional: true}
+- {name: staging_bucket, type: String, optional: true}
+outputs:
+- {name: model_name, type: String}
+- {name: model_dict, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'google-cloud-aiplatform==1.16.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'google-cloud-aiplatform==1.16.0'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI(
+          model_path,
+          sklearn_version = None,
+
+          display_name = None,
+          description = None,
+
+          # Uncomment when anyone requests these:
+          # instance_schema_uri: str = None,
+          # parameters_schema_uri: str = None,
+          # prediction_schema_uri: str = None,
+          # explanation_metadata: "google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata" = None,
+          # explanation_parameters: "google.cloud.aiplatform_v1.types.explanation.ExplanationParameters" = None,
+
+          project = None,
+          location = None,
+          labels = None,
+          # encryption_spec_key_name: str = None,
+          staging_bucket = None,
+      ):
+          import json
+          import os
+          import shutil
+          import tempfile
+          from google.cloud import aiplatform
+
+          if not location:
+              location = os.environ.get("CLOUD_ML_REGION")
+
+          if not labels:
+              labels = {}
+          labels["component-source"] = "github-com-ark-kun-pipeline-components"
+
+          # The serving container decides the model type based on the model file extension.
+          # So we need to rename the mode file (e.g. /tmp/inputs/model/data) to *.pkl
+          _, renamed_model_path = tempfile.mkstemp(suffix=".pkl")
+          shutil.copyfile(src=model_path, dst=renamed_model_path)
+
+          model = aiplatform.Model.upload_scikit_learn_model_file(
+              model_file_path=renamed_model_path,
+              sklearn_version=sklearn_version,
+
+              display_name=display_name,
+              description=description,
+
+              # instance_schema_uri=instance_schema_uri,
+              # parameters_schema_uri=parameters_schema_uri,
+              # prediction_schema_uri=prediction_schema_uri,
+              # explanation_metadata=explanation_metadata,
+              # explanation_parameters=explanation_parameters,
+
+              project=project,
+              location=location,
+              labels=labels,
+              # encryption_spec_key_name=encryption_spec_key_name,
+              staging_bucket=staging_bucket,
+          )
+          model_json = json.dumps(model.to_dict(), indent=2)
+          print(model_json)
+          return (model.resource_name, model_json)
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      def _serialize_str(str_value: str) -> str:
+          if not isinstance(str_value, str):
+              raise TypeError('Value "{}" has type "{}" instead of str.'.format(str(str_value), str(type(str_value))))
+          return str_value
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Upload Scikit learn pickle model to Google Cloud Vertex AI', description='')
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--sklearn-version", dest="sklearn_version", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--display-name", dest="display_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--description", dest="description", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--project", dest="project", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--location", dest="location", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--labels", dest="labels", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--staging-bucket", dest="staging_bucket", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=2)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = upload_Scikit_learn_pickle_model_to_Google_Cloud_Vertex_AI(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_str,
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --model
+    - {inputPath: model}
+    - if:
+        cond: {isPresent: sklearn_version}
+        then:
+        - --sklearn-version
+        - {inputValue: sklearn_version}
+    - if:
+        cond: {isPresent: display_name}
+        then:
+        - --display-name
+        - {inputValue: display_name}
+    - if:
+        cond: {isPresent: description}
+        then:
+        - --description
+        - {inputValue: description}
+    - if:
+        cond: {isPresent: project}
+        then:
+        - --project
+        - {inputValue: project}
+    - if:
+        cond: {isPresent: location}
+        then:
+        - --location
+        - {inputValue: location}
+    - if:
+        cond: {isPresent: labels}
+        then:
+        - --labels
+        - {inputValue: labels}
+    - if:
+        cond: {isPresent: staging_bucket}
+        then:
+        - --staging-bucket
+        - {inputValue: staging_bucket}
+    - '----output-paths'
+    - {outputPath: model_name}
+    - {outputPath: model_dict}
@@ -0,0 +1,190 @@
+name: Upload Tensorflow model to Google Cloud Vertex AI
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/KFPv2_hell/components/google-cloud/Vertex_AI/Models/Upload_Tensorflow_model/workaround_for_buggy_KFPv2_compiler/component.yaml'}
+inputs:
+- {name: model, type: TensorflowSavedModel}
+- {name: tensorflow_version, type: String, optional: true}
+- name: use_gpu
+  type: Boolean
+  default: "False"
+  optional: true
+- {name: display_name, type: String, optional: true}
+- {name: description, type: String, optional: true}
+- {name: project, type: String, optional: true}
+- {name: location, type: String, optional: true}
+- {name: labels, type: JsonObject, optional: true}
+- {name: staging_bucket, type: String, optional: true}
+outputs:
+- {name: model_name, type: String}
+- {name: model_dict, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'google-cloud-aiplatform==1.16.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'google-cloud-aiplatform==1.16.0'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def upload_Tensorflow_model_to_Google_Cloud_Vertex_AI(
+          model_path,
+          tensorflow_version = None,
+          use_gpu = False,
+
+          display_name = None,
+          description = None,
+
+          # Uncomment when anyone requests these:
+          # instance_schema_uri: str = None,
+          # parameters_schema_uri: str = None,
+          # prediction_schema_uri: str = None,
+          # explanation_metadata: "google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata" = None,
+          # explanation_parameters: "google.cloud.aiplatform_v1.types.explanation.ExplanationParameters" = None,
+
+          project = None,
+          location = None,
+          labels = None,
+          # encryption_spec_key_name: str = None,
+          staging_bucket = None,
+      ):
+          import json
+          import os
+          from google.cloud import aiplatform
+
+          if not location:
+              location = os.environ.get("CLOUD_ML_REGION")
+
+          if not labels:
+              labels = {}
+          labels["component-source"] = "github-com-ark-kun-pipeline-components"
+
+          model = aiplatform.Model.upload_tensorflow_saved_model(
+              saved_model_dir=model_path,
+              tensorflow_version=tensorflow_version,
+              use_gpu=use_gpu,
+
+              display_name=display_name,
+              description=description,
+
+              # instance_schema_uri=instance_schema_uri,
+              # parameters_schema_uri=parameters_schema_uri,
+              # prediction_schema_uri=prediction_schema_uri,
+              # explanation_metadata=explanation_metadata,
+              # explanation_parameters=explanation_parameters,
+
+              project=project,
+              location=location,
+              labels=labels,
+              # encryption_spec_key_name=encryption_spec_key_name,
+              staging_bucket=staging_bucket,
+          )
+          model_json = json.dumps(model.to_dict(), indent=2)
+          print(model_json)
+          return (model.resource_name, model_json)
+
+      def _deserialize_bool(s) -> bool:
+          from distutils.util import strtobool
+          return strtobool(s) == 1
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      def _serialize_str(str_value: str) -> str:
+          if not isinstance(str_value, str):
+              raise TypeError('Value "{}" has type "{}" instead of str.'.format(str(str_value), str(type(str_value))))
+          return str_value
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Upload Tensorflow model to Google Cloud Vertex AI', description='')
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--tensorflow-version", dest="tensorflow_version", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--use-gpu", dest="use_gpu", type=_deserialize_bool, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--display-name", dest="display_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--description", dest="description", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--project", dest="project", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--location", dest="location", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--labels", dest="labels", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--staging-bucket", dest="staging_bucket", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=2)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = upload_Tensorflow_model_to_Google_Cloud_Vertex_AI(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_str,
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --model
+    - {inputPath: model}
+    - if:
+        cond: {isPresent: tensorflow_version}
+        then:
+        - --tensorflow-version
+        - {inputValue: tensorflow_version}
+    - if:
+        cond: {isPresent: use_gpu}
+        then:
+        - --use-gpu
+        - {inputValue: use_gpu}
+    - if:
+        cond: {isPresent: display_name}
+        then:
+        - --display-name
+        - {inputValue: display_name}
+    - if:
+        cond: {isPresent: description}
+        then:
+        - --description
+        - {inputValue: description}
+    - if:
+        cond: {isPresent: project}
+        then:
+        - --project
+        - {inputValue: project}
+    - if:
+        cond: {isPresent: location}
+        then:
+        - --location
+        - {inputValue: location}
+    - if:
+        cond: {isPresent: labels}
+        then:
+        - --labels
+        - {inputValue: labels}
+    - if:
+        cond: {isPresent: staging_bucket}
+        then:
+        - --staging-bucket
+        - {inputValue: staging_bucket}
+    - '----output-paths'
+    - {outputPath: model_name}
+    - {outputPath: model_dict}
@@ -0,0 +1,181 @@
+name: Upload XGBoost model to Google Cloud Vertex AI
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/google-cloud/Vertex_AI/Models/Upload_XGBoost_model/workaround_for_buggy_KFPv2_compiler/component.yaml'}
+inputs:
+- {name: model, type: XGBoostModel}
+- {name: xgboost_version, type: String, optional: true}
+- {name: display_name, type: String, optional: true}
+- {name: description, type: String, optional: true}
+- {name: project, type: String, optional: true}
+- {name: location, type: String, optional: true}
+- {name: labels, type: JsonObject, optional: true}
+- {name: staging_bucket, type: String, optional: true}
+outputs:
+- {name: model_name, type: String}
+- {name: model_dict, type: JsonObject}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'google-cloud-aiplatform==1.16.0' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3
+      -m pip install --quiet --no-warn-script-location 'google-cloud-aiplatform==1.16.0'
+      --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def upload_XGBoost_model_to_Google_Cloud_Vertex_AI(
+          model_path,
+          xgboost_version = None,
+
+          display_name = None,
+          description = None,
+
+          # Uncomment when anyone requests these:
+          # instance_schema_uri: str = None,
+          # parameters_schema_uri: str = None,
+          # prediction_schema_uri: str = None,
+          # explanation_metadata: "google.cloud.aiplatform_v1.types.explanation_metadata.ExplanationMetadata" = None,
+          # explanation_parameters: "google.cloud.aiplatform_v1.types.explanation.ExplanationParameters" = None,
+
+          project = None,
+          location = None,
+          labels = None,
+          # encryption_spec_key_name: str = None,
+          staging_bucket = None,
+      ):
+          import json
+          import os
+          import shutil
+          import tempfile
+          from google.cloud import aiplatform
+
+          if not location:
+              location = os.environ.get("CLOUD_ML_REGION")
+
+          if not labels:
+              labels = {}
+          labels["component-source"] = "github-com-ark-kun-pipeline-components"
+
+          # The serving container decides the model type based on the model file extension.
+          # So we need to rename the mode file (e.g. /tmp/inputs/model/data) to *.pkl
+          _, renamed_model_path = tempfile.mkstemp(suffix=".pkl")
+          shutil.copyfile(src=model_path, dst=renamed_model_path)
+
+          model = aiplatform.Model.upload_xgboost_model_file(
+              model_file_path=renamed_model_path,
+              xgboost_version=xgboost_version,
+
+              display_name=display_name,
+              description=description,
+
+              # instance_schema_uri=instance_schema_uri,
+              # parameters_schema_uri=parameters_schema_uri,
+              # prediction_schema_uri=prediction_schema_uri,
+              # explanation_metadata=explanation_metadata,
+              # explanation_parameters=explanation_parameters,
+
+              project=project,
+              location=location,
+              labels=labels,
+              # encryption_spec_key_name=encryption_spec_key_name,
+              staging_bucket=staging_bucket,
+          )
+          model_json = json.dumps(model.to_dict(), indent=2)
+          print(model_json)
+          return (model.resource_name, model_json)
+
+      def _serialize_json(obj) -> str:
+          if isinstance(obj, str):
+              return obj
+          import json
+          def default_serializer(obj):
+              if hasattr(obj, 'to_struct'):
+                  return obj.to_struct()
+              else:
+                  raise TypeError("Object of type '%s' is not JSON serializable and does not have .to_struct() method." % obj.__class__.__name__)
+          return json.dumps(obj, default=default_serializer, sort_keys=True)
+
+      def _serialize_str(str_value: str) -> str:
+          if not isinstance(str_value, str):
+              raise TypeError('Value "{}" has type "{}" instead of str.'.format(str(str_value), str(type(str_value))))
+          return str_value
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Upload XGBoost model to Google Cloud Vertex AI', description='')
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--xgboost-version", dest="xgboost_version", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--display-name", dest="display_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--description", dest="description", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--project", dest="project", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--location", dest="location", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--labels", dest="labels", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--staging-bucket", dest="staging_bucket", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("----output-paths", dest="_output_paths", type=str, nargs=2)
+      _parsed_args = vars(_parser.parse_args())
+      _output_files = _parsed_args.pop("_output_paths", [])
+
+      _outputs = upload_XGBoost_model_to_Google_Cloud_Vertex_AI(**_parsed_args)
+
+      _output_serializers = [
+          _serialize_str,
+          _serialize_json,
+
+      ]
+
+      import os
+      for idx, output_file in enumerate(_output_files):
+          try:
+              os.makedirs(os.path.dirname(output_file))
+          except OSError:
+              pass
+          with open(output_file, 'w') as f:
+              f.write(_output_serializers[idx](_outputs[idx]))
+    args:
+    - --model
+    - {inputPath: model}
+    - if:
+        cond: {isPresent: xgboost_version}
+        then:
+        - --xgboost-version
+        - {inputValue: xgboost_version}
+    - if:
+        cond: {isPresent: display_name}
+        then:
+        - --display-name
+        - {inputValue: display_name}
+    - if:
+        cond: {isPresent: description}
+        then:
+        - --description
+        - {inputValue: description}
+    - if:
+        cond: {isPresent: project}
+        then:
+        - --project
+        - {inputValue: project}
+    - if:
+        cond: {isPresent: location}
+        then:
+        - --location
+        - {inputValue: location}
+    - if:
+        cond: {isPresent: labels}
+        then:
+        - --labels
+        - {inputValue: labels}
+    - if:
+        cond: {isPresent: staging_bucket}
+        then:
+        - --staging-bucket
+        - {inputValue: staging_bucket}
+    - '----output-paths'
+    - {outputPath: model_name}
+    - {outputPath: model_dict}
@@ -0,0 +1,35 @@
+name: Download from GCS
+inputs:
+- {name: GCS path, type: String}
+outputs:
+- {name: Data}
+metadata:
+  annotations:
+    author: Alexey Volkov <alexey.volkov@ark-kun.com>
+    canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/google-cloud/storage/download/workaround_for_buggy_KFPv2_compiler/component.yaml'
+implementation:
+    container:
+        image: google/cloud-sdk
+        command:
+        - bash # Pattern comparison only works in Bash
+        - -ex
+        - -c
+        - |
+            if [ -n "${GOOGLE_APPLICATION_CREDENTIALS}" ]; then
+                gcloud auth activate-service-account --key-file="${GOOGLE_APPLICATION_CREDENTIALS}"
+            fi
+
+            uri="$0"
+            output_path="$1"
+
+            # Checking whether the URI points to a single blob, a directory or a URI pattern
+            # URI points to a blob when that URI does not end with slash and listing that URI only yields the same URI
+            if [[ "$uri" != */ ]] && (gsutil ls "$uri" | grep --fixed-strings --line-regexp "$uri"); then
+                mkdir -p "$(dirname "$output_path")"
+                gsutil -m cp -r "$uri" "$output_path"
+            else
+                mkdir -p "$output_path" # When source path is a directory, gsutil requires the destination to also be a directory
+                gsutil -m rsync -r "$uri" "$output_path" # gsutil cp has different path handling than Linux cp. It always puts the source directory (name) inside the destination directory. gsutil rsync does not have that problem.
+            fi
+        - inputValue: GCS path
+        - outputPath: Data
@@ -0,0 +1,113 @@
+name: Binarize column using Pandas on CSV data
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/pandas/Binarize_column/in_CSV_format/component.yaml'}
+inputs:
+- {name: table, type: CSV}
+- {name: column_name, type: String}
+- {name: predicate, type: String, default: '> 0', optional: true}
+- {name: new_column_name, type: String, optional: true}
+- name: keep_original_column
+  type: Boolean
+  default: "False"
+  optional: true
+outputs:
+- {name: transformed_table, type: CSV}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'pandas==1.4.3' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet
+      --no-warn-script-location 'pandas==1.4.3' --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def binarize_column_using_Pandas_on_CSV_data(
+          table_path,
+          transformed_table_path,
+          column_name,
+          predicate = "> 0",
+          new_column_name = None,
+          keep_original_column = False,
+      ):
+          import pandas
+
+          df = pandas.read_csv(table_path).convert_dtypes()
+          original_series = df[column_name]
+
+          # Dynamically executing the predicate code
+          # Variable namespace for code execution
+          namespace = dict(x=original_series)
+          # I though that there should be no space before `predicate` so that "dot" predicate methods like ".between(min, max)" work.
+          # However Python allows spaces before dot: `df .isna()`.
+          # So having a space is not a problem
+          transform_code = f"""new_series_boolean = x {predicate}"""
+          # Note: exec() takes no keyword arguments
+          # exec(__source=transform_code, __globals=namespace)
+          exec(transform_code, namespace)
+          new_series_boolean = namespace["new_series_boolean"]
+
+          # There are multiple ways to convert boolean column to integer.
+          # .apply(int) might be faster. https://stackoverflow.com/a/49804868/1497385
+          # TODO: Do a proper benchmark.
+          new_series = new_series_boolean.apply(int)
+          # new_series = new_series_boolean.astype(int)
+          # new_series = new_series_boolean.replace({False: 0, True: 1})
+
+          if new_column_name:
+              df.insert(loc=0, column=new_column_name, value=new_series)
+              if not keep_original_column:
+                  df = df.drop(columns=[column_name])
+          else:
+              df[column_name] = new_series
+
+          df.to_csv(transformed_table_path, index=False)
+
+      def _deserialize_bool(s) -> bool:
+          from distutils.util import strtobool
+          return strtobool(s) == 1
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Binarize column using Pandas on CSV data', description='')
+      _parser.add_argument("--table", dest="table_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--column-name", dest="column_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--predicate", dest="predicate", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--new-column-name", dest="new_column_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--keep-original-column", dest="keep_original_column", type=_deserialize_bool, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--transformed-table", dest="transformed_table_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = binarize_column_using_Pandas_on_CSV_data(**_parsed_args)
+    args:
+    - --table
+    - {inputPath: table}
+    - --column-name
+    - {inputValue: column_name}
+    - if:
+        cond: {isPresent: predicate}
+        then:
+        - --predicate
+        - {inputValue: predicate}
+    - if:
+        cond: {isPresent: new_column_name}
+        then:
+        - --new-column-name
+        - {inputValue: new_column_name}
+    - if:
+        cond: {isPresent: keep_original_column}
+        then:
+        - --keep-original-column
+        - {inputValue: keep_original_column}
+    - --transformed-table
+    - {outputPath: transformed_table}
@@ -0,0 +1,75 @@
+name: Fill all missing values using Pandas on CSV data
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/pandas/Fill_all_missing_values/in_CSV_format/component.yaml'}
+inputs:
+- {name: table, type: CSV}
+- {name: replacement_value, type: String, default: '0', optional: true}
+- {name: column_names, type: JsonArray, optional: true}
+outputs:
+- {name: transformed_table, type: CSV}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'pandas==1.4.1' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet
+      --no-warn-script-location 'pandas==1.4.1' --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def fill_all_missing_values_using_Pandas_on_CSV_data(
+          table_path,
+          transformed_table_path,
+          replacement_value = "0",
+          column_names = None,
+      ):
+          import pandas
+
+          df = pandas.read_csv(
+              table_path,
+              dtype="string",
+          )
+
+          for column_name in column_names or df.columns:
+              df[column_name] = df[column_name].fillna(value=replacement_value)
+
+          df.to_csv(
+              transformed_table_path, index=False,
+          )
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Fill all missing values using Pandas on CSV data', description='')
+      _parser.add_argument("--table", dest="table_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--replacement-value", dest="replacement_value", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--column-names", dest="column_names", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--transformed-table", dest="transformed_table_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = fill_all_missing_values_using_Pandas_on_CSV_data(**_parsed_args)
+    args:
+    - --table
+    - {inputPath: table}
+    - if:
+        cond: {isPresent: replacement_value}
+        then:
+        - --replacement-value
+        - {inputValue: replacement_value}
+    - if:
+        cond: {isPresent: column_names}
+        then:
+        - --column-names
+        - {inputValue: column_names}
+    - --transformed-table
+    - {outputPath: transformed_table}
@@ -0,0 +1,59 @@
+name: Select columns using Pandas on CSV data
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/pandas/Select_columns/in_CSV_format/component.yaml'}
+inputs:
+- {name: table, type: CSV}
+- {name: column_names, type: JsonArray}
+outputs:
+- {name: transformed_table, type: CSV}
+implementation:
+  container:
+    image: python:3.9
+    command:
+    - sh
+    - -c
+    - (PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet --no-warn-script-location
+      'pandas==1.4.2' || PIP_DISABLE_PIP_VERSION_CHECK=1 python3 -m pip install --quiet
+      --no-warn-script-location 'pandas==1.4.2' --user) && "$0" "$@"
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def select_columns_using_Pandas_on_CSV_data(
+          table_path,
+          transformed_table_path,
+          column_names,
+      ):
+          import pandas
+
+          df = pandas.read_csv(
+              table_path,
+              dtype="string",
+          )
+          df = df[column_names]
+          df.to_csv(transformed_table_path, index=False)
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Select columns using Pandas on CSV data', description='')
+      _parser.add_argument("--table", dest="table_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--column-names", dest="column_names", type=json.loads, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--transformed-table", dest="transformed_table_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = select_columns_using_Pandas_on_CSV_data(**_parsed_args)
+    args:
+    - --table
+    - {inputPath: table}
+    - --column-names
+    - {inputValue: column_names}
+    - --transformed-table
+    - {outputPath: transformed_table}
@@ -0,0 +1,102 @@
+name: Create fully connected tensorflow network
+description: Creates fully-connected network in Tensorflow SavedModel format
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/Create_fully_connected_network/component.yaml'}
+inputs:
+- {name: input_size, type: Integer}
+- {name: hidden_layer_sizes, type: JsonArray, default: '[]', optional: true}
+- {name: output_size, type: Integer, default: '1', optional: true}
+- {name: activation_name, type: String, default: relu, optional: true}
+- {name: output_activation_name, type: String, optional: true}
+- {name: random_seed, type: Integer, default: '0', optional: true}
+outputs:
+- {name: model, type: TensorflowSavedModel}
+implementation:
+  container:
+    image: tensorflow/tensorflow:2.7.0
+    command:
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def create_fully_connected_tensorflow_network(
+          input_size,
+          model_path,
+          hidden_layer_sizes = [],
+          output_size = 1,
+          activation_name = "relu",
+          output_activation_name = None,
+          random_seed = 0,
+      ):
+          """Creates fully-connected network in Tensorflow SavedModel format"""
+          import tensorflow as tf
+          tf.random.set_seed(seed=random_seed)
+
+          model = tf.keras.models.Sequential()
+          model.add(tf.keras.Input(shape=(input_size,)))
+          for layer_size in hidden_layer_sizes:
+              model.add(tf.keras.layers.Dense(units=layer_size, activation=activation_name))
+          # The last layer is left without activation
+          model.add(tf.keras.layers.Dense(units=output_size, activation=output_activation_name))
+
+          print(model.summary())
+
+          # Using tf.keras.models.save_model instead of tf.saved_model.save to prevent downstream error:
+          #tf.saved_model.save(model, model_path)
+          # ValueError: Unable to create a Keras model from this SavedModel.
+          # This SavedModel was created with `tf.saved_model.save`, and lacks the Keras metadata.
+          # Please save your Keras model by calling `model.save`or `tf.keras.models.save_model`.
+          # See https://github.com/keras-team/keras/issues/16451
+          tf.keras.models.save_model(model, model_path)
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Create fully connected tensorflow network', description='Creates fully-connected network in Tensorflow SavedModel format')
+      _parser.add_argument("--input-size", dest="input_size", type=int, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--hidden-layer-sizes", dest="hidden_layer_sizes", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--output-size", dest="output_size", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--activation-name", dest="activation_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--output-activation-name", dest="output_activation_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--random-seed", dest="random_seed", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = create_fully_connected_tensorflow_network(**_parsed_args)
+    args:
+    - --input-size
+    - {inputValue: input_size}
+    - if:
+        cond: {isPresent: hidden_layer_sizes}
+        then:
+        - --hidden-layer-sizes
+        - {inputValue: hidden_layer_sizes}
+    - if:
+        cond: {isPresent: output_size}
+        then:
+        - --output-size
+        - {inputValue: output_size}
+    - if:
+        cond: {isPresent: activation_name}
+        then:
+        - --activation-name
+        - {inputValue: activation_name}
+    - if:
+        cond: {isPresent: output_activation_name}
+        then:
+        - --output-activation-name
+        - {inputValue: output_activation_name}
+    - if:
+        cond: {isPresent: random_seed}
+        then:
+        - --random-seed
+        - {inputValue: random_seed}
+    - --model
+    - {outputPath: model}
@@ -0,0 +1,100 @@
+name: Predict with TensorFlow model on CSV data
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/Predict/on_CSV/component.yaml'}
+inputs:
+- {name: dataset, type: CSV}
+- {name: model, type: TensorflowSavedModel}
+- {name: label_column_name, type: String, optional: true}
+- {name: batch_size, type: Integer, default: '1000', optional: true}
+outputs:
+- {name: predictions}
+implementation:
+  container:
+    image: tensorflow/tensorflow:2.9.1
+    command:
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def predict_with_TensorFlow_model_on_CSV_data(
+          dataset_path,
+          model_path,
+          predictions_path,
+          label_column_name = None,
+          batch_size = 1000,
+      ):
+          import numpy
+          import tensorflow as tf
+
+          model = tf.saved_model.load(export_dir=model_path)
+
+          dataset = tf.data.experimental.make_csv_dataset(
+              file_pattern=dataset_path,
+              batch_size=batch_size,
+              label_name=label_column_name,
+              header=True,
+              num_epochs=1,
+              shuffle=False,
+              ignore_errors=False,
+          )
+
+          def stack_feature_batches(features_batch):
+              # Need to stack individual feature columns to create a single feature tensor
+              # Need to cast all column tensor types to float to prevent errors.
+              list_of_feature_batches = list(
+                  tf.cast(x=feature_batch, dtype=tf.float32)
+                  for feature_batch in features_batch.values()
+              )
+              return tf.stack(list_of_feature_batches, axis=-1)
+
+          def transform_features_and_drop_labels(features_batch, labels_batch):
+              return stack_feature_batches(features_batch)
+
+          dataset_map_fn = (
+              transform_features_and_drop_labels
+              if label_column_name
+              else stack_feature_batches
+          )
+
+          dataset = dataset.map(dataset_map_fn)
+
+          with open(predictions_path, "w") as predictions_file:
+              for features_batch in dataset:
+                  predictions_tensor = model(features_batch)
+                  numpy.savetxt(predictions_file, predictions_tensor.numpy())
+
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Predict with TensorFlow model on CSV data', description='')
+      _parser.add_argument("--dataset", dest="dataset_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--batch-size", dest="batch_size", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--predictions", dest="predictions_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = predict_with_TensorFlow_model_on_CSV_data(**_parsed_args)
+    args:
+    - --dataset
+    - {inputPath: dataset}
+    - --model
+    - {inputPath: model}
+    - if:
+        cond: {isPresent: label_column_name}
+        then:
+        - --label-column-name
+        - {inputValue: label_column_name}
+    - if:
+        cond: {isPresent: batch_size}
+        then:
+        - --batch-size
+        - {inputValue: batch_size}
+    - --predictions
+    - {outputPath: predictions}
@@ -0,0 +1,170 @@
+name: Train model using Keras on CSV
+metadata:
+  annotations: {author: Alexey Volkov <alexey.volkov@ark-kun.com>, canonical_location: 'https://raw.githubusercontent.com/Ark-kun/pipeline_components/master/components/tensorflow/Train_model_using_Keras/on_CSV/component.yaml'}
+inputs:
+- {name: training_data, type: CSV}
+- {name: model, type: TensorflowSavedModel}
+- {name: label_column_name, type: String}
+- {name: loss_function_name, type: String, default: mean_squared_error, optional: true}
+- {name: number_of_epochs, type: Integer, default: '1', optional: true}
+- {name: learning_rate, type: Float, default: '0.1', optional: true}
+- {name: optimizer_name, type: String, default: Adadelta, optional: true}
+- {name: optimizer_parameters, type: JsonObject, optional: true}
+- {name: batch_size, type: Integer, default: '32', optional: true}
+- {name: metric_names, type: JsonArray, optional: true}
+- {name: random_seed, type: Integer, default: '0', optional: true}
+outputs:
+- {name: trained_model, type: TensorflowSavedModel}
+implementation:
+  container:
+    image: tensorflow/tensorflow:2.8.0
+    command:
+    - sh
+    - -ec
+    - |
+      program_path=$(mktemp)
+      printf "%s" "$0" > "$program_path"
+      python3 -u "$program_path" "$@"
+    - |
+      def _make_parent_dirs_and_return_path(file_path: str):
+          import os
+          os.makedirs(os.path.dirname(file_path), exist_ok=True)
+          return file_path
+
+      def train_model_using_Keras_on_CSV(
+          training_data_path,
+          model_path,
+          trained_model_path,
+          label_column_name,
+          loss_function_name = "mean_squared_error",
+          number_of_epochs = 1,
+          learning_rate = 0.1,
+          optimizer_name = "Adadelta",
+          optimizer_parameters = None,
+          batch_size = 32,
+          metric_names = None,
+          random_seed = 0,
+      ):
+          import tensorflow as tf
+          tf.random.set_seed(seed=random_seed)
+
+          # Loading model using Keras. Model loaded using TensorFlow does not have .fit.
+          #model = tf.saved_model.load(export_dir=model_path)
+          keras_model = tf.keras.models.load_model(filepath=model_path)
+
+          optimizer_parameters = optimizer_parameters or {}
+          optimizer_parameters["learning_rate"] = learning_rate
+          optimizer_config = {
+              "class_name": optimizer_name,
+              "config": optimizer_parameters,
+          }
+          optimizer = tf.keras.optimizers.get(optimizer_config)
+          loss = tf.keras.losses.get(loss_function_name)
+
+          training_dataset = tf.data.experimental.make_csv_dataset(
+              file_pattern=training_data_path,
+              batch_size=batch_size,
+              label_name=label_column_name,
+              header=True,
+              # Need to specify num_epochs=1 otherwise the training becomes infinite
+              num_epochs=1,
+              shuffle=True,
+              shuffle_seed=random_seed,
+              ignore_errors=True,
+          )
+          def stack_feature_batches(features_batch, labels_batch):
+              # Need to stack individual feature columns to create a single feature tensor
+              # Need to cast all column tensor types to float to prevent error:
+              # TypeError: Tensors in list passed to 'values' of 'Pack' Op have types [int32, float32, float32, int32, int32] that don't all match.
+              list_of_feature_batches = list(tf.cast(x=feature_batch, dtype=tf.float32) for feature_batch in features_batch.values())
+              return tf.stack(list_of_feature_batches, axis=-1), labels_batch
+
+          training_dataset = training_dataset.map(stack_feature_batches)
+
+          # Need to compile the model to prevent error:
+          # ValueError: No gradients provided for any variable: [..., ...].
+          keras_model.compile(
+              optimizer=optimizer,
+              loss=loss,
+              metrics=metric_names,
+          )
+          keras_model.fit(
+              training_dataset,
+              epochs=number_of_epochs,
+          )
+
+          # Using tf.keras.models.save_model instead of tf.saved_model.save to prevent downstream error:
+          #tf.saved_model.save(keras_model, trained_model_path)
+          # ValueError: Unable to create a Keras model from this SavedModel.
+          # This SavedModel was created with `tf.saved_model.save`, and lacks the Keras metadata.
+          # Please save your Keras model by calling `model.save`or `tf.keras.models.save_model`.
+          # See https://github.com/keras-team/keras/issues/16451
+          tf.keras.models.save_model(keras_model, trained_model_path)
+
+      import json
+      import argparse
+      _parser = argparse.ArgumentParser(prog='Train model using Keras on CSV', description='')
+      _parser.add_argument("--training-data", dest="training_data_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--model", dest="model_path", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--label-column-name", dest="label_column_name", type=str, required=True, default=argparse.SUPPRESS)
+      _parser.add_argument("--loss-function-name", dest="loss_function_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--number-of-epochs", dest="number_of_epochs", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--learning-rate", dest="learning_rate", type=float, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimizer-name", dest="optimizer_name", type=str, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--optimizer-parameters", dest="optimizer_parameters", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--batch-size", dest="batch_size", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--metric-names", dest="metric_names", type=json.loads, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--random-seed", dest="random_seed", type=int, required=False, default=argparse.SUPPRESS)
+      _parser.add_argument("--trained-model", dest="trained_model_path", type=_make_parent_dirs_and_return_path, required=True, default=argparse.SUPPRESS)
+      _parsed_args = vars(_parser.parse_args())
+
+      _outputs = train_model_using_Keras_on_CSV(**_parsed_args)
+    args:
+    - --training-data
+    - {inputPath: training_data}
+    - --model
+    - {inputPath: model}
+    - --label-column-name
+    - {inputValue: label_column_name}
+    - if:
+        cond: {isPresent: loss_function_name}
+        then:
+        - --loss-function-name
+        - {inputValue: loss_function_name}
+    - if:
+        cond: {isPresent: number_of_epochs}
+        then:
+        - --number-of-epochs
+        - {inputValue: number_of_epochs}
+    - if:
+        cond: {isPresent: learning_rate}
+        then:
+        - --learning-rate
+        - {inputValue: learning_rate}
+    - if:
+        cond: {isPresent: optimizer_name}
+        then:
+        - --optimizer-name
+        - {inputValue: optimizer_name}
+    - if:
+        cond: {isPresent: optimizer_parameters}
+        then:
+        - --optimizer-parameters
+        - {inputValue: optimizer_parameters}
+    - if:
+        cond: {isPresent: batch_size}
+        then:
+        - --batch-size
+        - {inputValue: batch_size}
+    - if:
+        cond: {isPresent: metric_names}
+        then:
+        - --metric-names
+        - {inputValue: metric_names}
+    - if:
+        cond: {isPresent: random_seed}
+        then:
+        - --random-seed
+        - {inputValue: random_seed}
+    - --trained-model
+    - {outputPath: trained_model}
@@ -0,0 +1,102 @@
+# Administrative Howto notes on CI Notebook Ingestion
+
+
+This readme covers administrative actions that are performed on an as-needed basis.
+
+## Team: vertex-ai-owners
+
+Members of the vertex-ai-owners (git team) have administrative privileges. 
+
+
+### Viewing members
+
+1. Goto the repo
+2. From top-level menu, select: (Settings -> Collaborators and Teams)[https://github.com/GoogleCloudPlatform/vertex-ai-samples/settings/access]
+
+
+### Adding a new member
+
+If another member needs to be added:
+   - Have the new member make a request to join the team.
+   - vertex-ai-owners with the `Maintainer` tag may add the new member.
+
+   
+## Executing CI notebook ingestion checks on a PR
+   
+### Killing a stuck PR
+
+If the CI notebook ingestion test is stuck (not terminating), you can kill the process by:
+
+1. Goto the PR
+2. Under checks, find the entry: vertex-ai-notebook-execution-test (python-docs-samples-tests) In progress —> Summary
+3. Select Details
+4. At bottom of details page, select: View more details on Google Cloud Build
+5. In Cloud Build history page, select Cancel on the top menu bar.
+
+### Restart a PR test
+
+There are two ways to restart the CI notebook ingestion tests on an open PR.
+
+1. In Cloud Build history page, select Rebuild on the top menu bar.
+2. or, in a comment in the PR enter: /gcbrun
+
+## Bypassing CI notebook ingestion checks on a PR
+
+We strongly discourage this, unless there is a compelling reason that would impact the integrity of the quality process.
+
+There are two ways of doing this. In both cases, you do:
+
+1. Goto the repo
+2. From top-level menu, select: (Settings -> Branches)[https://github.com/GoogleCloudPlatform/vertex-ai-samples/settings/branches]
+3. Under Branch Protection Rules, select the `main` branch.
+
+### Allowing a member to disable requirements for merging
+
+Specific member(s) can be assigned the ability to override requirements and merge a PR, by:
+
+1. Select Edit for the `main` branch in Branch Protection Rules.
+2. Find the entry "Allow specified actors to bypass required pull requests".
+3. Under this entry, add the member's git LDAP.
+4. Select SAVE.
+5. The "Squash and Merge" button will now be enabled on all PRs viewed by that member.
+
+### Temporarily disable checks.
+
+You can disable requirement checks temporarily on all PRs. 
+
+1. Select Edit for the `main` branch in Branch Protection Rules.
+2. Uncheck:
+  - Require approvals
+  - Require review from Code Owners
+  - Require status checks to pass before merging
+3. Select SAVE
+4. Now all members will see a green "Squash and Merge" on all PRs viewed by that member.
+
+To reverse, recheck the settings you unchecked above.
+
+## Linting
+
+To execute the identical lint image locally, from the CI notebook ingestion checks, do:
+
+1. Goto the corresponding local folder in the repo.
+2. Run: `docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest <your_notebooks>`
+
+## Install dependency issues
+
+Some packages (and combinations) have dependencies that fail on the virgin VM image used for the CI notebook ingestion test.
+
+### TFDV
+
+If the notebook installs and uses tensorflow_data_validation, install as follows:
+
+! pip3 install -q {USER_FLAG} google-cloud-aiplatform \
+                              tensorflow-data-validation \
+                              protobuf==3.20.3
+
+! pip3 install -q {USER_FLAG} cachetools==5.2.0
+
+
+
+
+   
+   
@@ -18,6 +18,7 @@
 /matching_engine/matching_engine_for_indexing.ipynb @yinghsienwu
 /matching_engine/stream_update_for_matching_engine.ipynb @peterping666
 /sdk/pytorch_lightning_custom_container_training.ipynb @brianchunkang
+/sdk/sdk_pytorch_torchrun_custom_container_training_imagenet.ipynb @brianchunkang
 /tensorboard @yfang1
 /feature_store @nayaknishant @morgandu
 /prediction @googleapis/vertex-prediction-team
@@ -34,4 +35,5 @@
 /notebooks/community/vertex-ai-samples/notebooks/community/model_registry/vertex_ai_model_registry_bqml_custom_model_versioning.ipynb @inardini
 /notebooks/community/vertex-ai-samples/notebooks/community/model_registry/vertex_ai_model_registry_automl_model_versioning.ipynb @inardini
 /notebooks/community/vizier/conversions_vertex_vizier_and_open_source_vizier.ipynb @halio-g
-/notebooks/community/experiments/vertex_ai_model_experimentation.ipynb @inardini @asobran
+/notebooks/community/experiments/vertex_ai_model_experimentation.ipynb @inardini @asobran
+/notebooks/community/pipelines/google_cloud_pipeline_components_bqml_pipeline_anomaly_detection.ipynb @inardini
@@ -33,7 +33,7 @@
        "\n",
        "  <td>\n",
        "    <a href=\"https://console.cloud.google.com/vertex-ai/notebooks/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/matching_engine/stream_update_matching_engine.ipynb\">\n",
-        "      Run in Google Cloud Notebooks\n",
+        "      Run in Workbench AI Notebooks\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
@@ -53,7 +53,7 @@
      "source": [
        "## Overview\n",
        "\n",
-        "This example demonstrates how to use the GCP matching engine Stream Update Service. \n",
+        "This example demonstrates how to use the Vertex AI Matching Engine Stream Update Service. \n",
        "\n",
        "### Dataset\n",
        "\n",
@@ -150,7 +150,7 @@
      "source": [
        "### Installation\n",
        "\n",
-        "Download and install the latest (preview) version of the Vertex SDK for Python."
+        "Download and install the latest (preview) version of the Vertex AI SDK for Python."
      ]
    },
    {
@@ -442,7 +442,7 @@
        "id": "8292bcedab58"
      },
      "source": [
-        "## Prepare the Data\n",
+        "## Prepare the data\n",
        "\n",
        "The GloVe dataset consists of a set of pre-trained embeddings. The embeddings are split into a \"train\" split, and a \"test\" split.\n",
        "We will create a vector search index from the \"train\" split, and use the embedding vectors in the \"test\" split as query vectors to test the vector search index.\n",
@@ -525,7 +525,7 @@
        "        f.write('{\"id\":\"' + str(i) + '\",')\n",
        "        f.write('\"embedding\":[' + \",\".join(str(x) for x in train[i]) + \"],\")\n",
        "        f.write(\n",
-        "            '\"restricts\":[{\"namespace\": \"class\", \"allow_list\": [\"' + str(i) + '\"]}],'\n",
+        "            '\"restricts\":[{\"namespace\": \"class\", \"allow\": [\"' + str(i) + '\"]}],'\n",
        "        )\n",
        "        f.write('\"crowding_tag\":' + ('\"a\"' if i % 2 == 0 else '\"b\"') + \"}\")\n",
        "        f.write(\"\\n\")\n",
@@ -854,7 +854,7 @@
        "id": "00c606bc97b5"
      },
      "source": [
-        "## Create Online Queries\n",
+        "## Create online queries\n",
        "\n",
        "After you built your indexes, you may query against the deployed index through the online querying gRPC API (Match service) within the virtual machine instances from the same region (for example 'us-central1' in this tutorial).  \n",
        "\n",
@@ -28,9 +28,11 @@ The first stage in MLOps is the collection and preparation for the purpose of de

 ### Get Started

-[Get started with Dataflow](community/ml_ops/stage1/get_started_dataflow.ipynb)

-In this tutorial, you learn how to use `Dataflow` for training with `Vertex AI`.
+[Get started with Dataflow](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_dataflow.ipynb)
+
+```
+Learn how to use `Dataflow` for training with `Vertex AI`.

 The steps performed include:

@@ -40,10 +42,13 @@ The steps performed include:
 - Upstream preprocessing of data:
    - tabular data
    - image data
+```

-[Get started with Vertex AI datasets](community/ml_ops/stage1/get_started_vertex_datasets.ipynb)

-In this tutorial, you learn how to use `Vertex AI Dataset` for training with `Vertex AI`.
+[Get started with Vertex AI datasets](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_vertex_datasets.ipynb)
+
+```
+Learn how to use `Vertex AI Dataset` for training with `Vertex AI`.

 The steps performed include:

@@ -61,10 +66,13 @@ The steps performed include:
 - Detect anomalies in new data using TensorFlow Data Validation.
 - Generate a TFRecord feature specification using TensorFlow Transform from the data schema.
 - Export a dataset and convert to TFRecords.
+```

-[Get started with BigQuery datasets](community/ml_ops/stage1/get_started_bq_datasets.ipynb)

-In this tutorial, you learn how to use `BigQuery` as a dataset for training with `Vertex AI`.
+[Get started with BigQuery datasets](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_bq_datasets.ipynb)
+
+```
+Learn how to use `BigQuery` as a dataset for training with `Vertex AI`.

 The steps performed include:

@@ -75,10 +83,13 @@ The steps performed include:
 - Select rows from extracted CSV files into a `tf.data.Dataset` -- compatible for custom training `TensorFlow` models.
 - Create a `BigQuery` dataset from CSV files.
 - Extract data from `BigQuery` table into a `DMatrix` -- compatible for custom training `XGBoost` models.
+```

-[Get started with Vertex AI Data Labeling](community/ml_ops/stage1/get_started_with_data_labeling.ipynb)

-In this tutorial, you learn how to use the `Vertex AI Data Labeling` service.
+[Get started with Vertex AI Data Labeling](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_with_data_labeling.ipynb)
+
+```
+Learn how to use the `Vertex AI Data Labeling` service/

 The steps performed include:

@@ -87,28 +98,31 @@ The steps performed include:
 - Submit the data labeling job.
 - List data labeling jobs.
 - Cancel a data labeling job.
+```


+[Create an unlabelled Vertex AI AutoML text entity extraction dataset from PDFs using Vision API](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/get_started_with_visionapi_and_vertex_datasets.ipynb)

-[Create an unlabelled Vertex AI AutoML text entity extraction dataset from PDFs using Vision API](community/ml_ops/stage1/get_started_with_visionapi_and_vertex_datasets.ipynb)
-
-In this tutorial, you learn to use `Vision API` to extract text from PDF files stored on a Cloud Storage bucket. You then process the results and create an unlabelled `Vertex AI Dataset`, compatible with `AutoML`, for text entity extraction.
+```
+Learn to use `Vision API` to extract text from PDF files stored on a Cloud Storage bucket.

 The steps performed include:

 1. Using `Vision API` to perform Optical Character Recognition (OCR) to extract text from PDF files.
 2. Processing the results and saving them to text files.
 3. Generating a `Vertex AI Dataset` import file.
-4. Creating a new unlabelled text entity extraction `Vertex AI Dataset` resource in `Vertex AI`.
+4. Cr


 ### E2E Stage Example

-[Stage 1: Data Management](mlops_data_management.ipynb)
-
+[Data management](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage1/mlops_data_management.ipynb)

 ```
+In this tutorial, you create a MLOps stage 1: data management process.
+
 The steps performed include:
+
 - Explore and visualize the data.
 - Create a Vertex AI `Dataset` resource from `BigQuery` table -- for AutoML training.
 - Extract a copy of the dataset to a CSV file in Cloud Storage.
@@ -117,4 +131,4 @@ The steps performed include:
 - Generate statistics and data schema using TensorFlow Data Validation from the samples in the dataframe.
 - Generate a TFRecord feature specification using TensorFlow Data Validation from the data schema.
 - Preprocess a portion of the BigQuery data using `Dataflow` -- for custom training.
-```
+```
@@ -35,9 +35,10 @@ The second stage in MLOps is experimenting in developing one or more baseline mo

 ### Get Started

-[Get started with Vertex AI Training for R](community/ml_ops/stage2/get_started_vertex_training_r.ipynb)
+[Get started with Vertex AI Training for R](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_r.ipynb)

-In this tutorial, you learn how to use `Vertex AI Training` for training a R custom model.
+```
+Learn how to use `Vertex AI Training` for training a R custom model.

 The steps performed include:

@@ -51,18 +52,26 @@ The steps performed include:
 - Create a training image for training the model.
 - Train a R model using `Vertex AI Trainingh` service with the R-to-Python training package.

-[Get started with Logging](community/ml_ops/stage2/get_started_with_logging.ipynb)
+```

-In this tutorial, you learn how to use Python and Cloud logging awhen training with `Vertex AI`.
+
+[Get started with Logging](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_logging.ipynb)
+
+```
+Learn how to use Python and Cloud logging when training with `Vertex AI`.

 The steps performed include:

 - Use Python logging to log training configuration/results locally.
 - Use Google Cloud Logging to log training configuration/results in cloud storage.

-[Get started with Vertex AI Hyperparameter Tuning for XGBoost] (community/ml_ops/stage2/get_started_vertex_hpt_xgboost.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Hyperparameter Tuning` for training a XGBoost custom model.
+
+[Get started with Vertex AI Hyperparameter Tuning for XGBoost](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_hpt_xgboost.ipynb)
+
+```
+Learn how to use `Vertex AI Hyperparameter Tuning` for training a XGBoost custom model.

 The steps performed include:

@@ -71,9 +80,13 @@ The steps performed include:
 - Save the model artifacts to Cloud Storage using GCSFuse.
 - Create a `Vertex AI Model` resource.

-[Get started with Vertex AI Training for XGBoost](community/ml_ops/stage2/get_started_vertex_training_xgboost.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Training` for training a XGBoost custom model.
+
+[Get started with Vertex AI Training for XGBoost](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_xgboost.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for training a XGBoost custom model.

 The steps performed include:

@@ -82,9 +95,13 @@ The steps performed include:
 - Save the model artifacts to Cloud Storage using GCSFuse.
 - Create a `Vertex AI Model` resource.

-[Get started with TabNet builtin algorithm for training tabular models](community/ml_ops/stage2/get_started_with_tabnet.ipynb)
+```

-In this notebook, you learn how to run `Vertex AI TabNet` built algorithm for training custom tabular models.
+
+[Get started with TabNet builtin algorithm for training tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_tabnet.ipynb)
+
+```
+Learn how to run `Vertex AI TabNet` built algorithm for training custom tabular models.

 The steps performed include:

@@ -97,9 +114,13 @@ The steps performed include:
 - Hyperparameter tuning the `Vertex AI TabNet` model.
 - Train the model using `Vertex AI Training` using BigQuery table.

-[Get started with prebuilt TFHub models](community/ml_ops/stage2/get_started_with_tfhub_models.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Training` with prebuilt models from TensorFlow Hub.
+
+[Get started with prebuilt TFHub models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_tfhub_models.ipynb)
+
+```
+Learn how to use `Vertex AI Training` with prebuilt models from TensorFlow Hub.

 The steps performed include:

@@ -112,23 +133,31 @@ The steps performed include:
    - Train then model
    - Save model artifacts and upload as Vertex AI Model resource.

-[Get started with BigQuery ML Training](community/ml_ops/stage2/get_started_bqml_training.ipynb)
+```

-In this tutorial, you learn how to use `BigQueryML` (BQML) for training with `Vertex AI`.
+
+[Get started with BigQuery ML Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_bqml_training.ipynb)
+
+```
+Learn how to use `BigQueryML` for training with `Vertex AI`.

 The steps performed include:

 - Create a local BigQuery table in your project
- Train a BQML model
- Evaluate the BQML model
- Export the BQML model as a cloud model
+- Train a BigQuery ML model
+- Evaluate the BigQuery ML model
+- Export the BigQuery ML model as a cloud model
 - Upload the exported model as a `Vertex AI Model` resource
- Hyperparameter tune a BQML model with `Vertex AI Vizier`
- Automatically register a BQML model to `Vertex AI Model Registry`
+- Hyperparameter tune a BigQuery ML model with `Vertex AI Vizier`
+- Automatically register a BigQuery ML model to `Vertex AI Model Registry`

-[Get started with Vertex AI Vizier](community/ml_ops/stage2/get_started_vertex_vizier.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Vizier` for when training with `Vertex AI`.
+
+[Get started with Vertex AI Vizier](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_vizier.ipynb)
+
+```
+Learn how to use `Vertex AI Vizier` for when training with `Vertex AI`.

 The steps performed include:

@@ -136,9 +165,13 @@ The steps performed include:
 - Hyperparameter tuning with Vizier (Bayesian) algorithm.
 - Suggesting trials and updating results for Vizier study

-[Get started with distributed training using DASK](community/ml_ops/stage2/get_started_with_distributed_training_xgboost.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Training` for distributed training of XGBoost model using the OSS package DASK. Additionally, you learn to construct and deploy a custom serving container using a Flask web server.
+
+[Get started with distributed training using DASK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_distributed_training_xgboost.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for distributed training of XGBoost model using the OSS package DASK.

 The steps performed include:

@@ -152,9 +185,13 @@ The steps performed include:
 - Deploy the `Vertex AI Model` resource to `Vertex AI Endpoint` resource.
 - Make a prediction.

-[Get started with Vertex AI TensorBoard](community/ml_ops/stage2/get_started_vertex_tensorboard.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI TensorBoard` when training with `Vertex AI`.
+
+[Get started with Vertex AI TensorBoard](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_tensorboard.ipynb)
+
+```
+Learn how to use `Vertex AI TensorBoard` when training with `Vertex AI`.

 The steps performed include:

@@ -162,9 +199,13 @@ The steps performed include:
 - Using TensorBoard with locally trained model.
 - Using Vertex AI TensorBoard with Vertex AI Training.

-[Get started with Vertex AI Training for R using R Kernel](community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI`, using an R kernel, for training and deploying an R custom model.
+
+[Get started with Vertex AI Training for R using R Kernel](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb)
+
+```
+Learn how to use `Vertex AI`, using an R kernel, for training and deploying an R custom model.

 The steps performed include:

@@ -176,10 +217,13 @@ The steps performed include:
 - Deploy the `Model` resource (trained R model) to the `Endpoint` resource.
 - Make an online prediction.

+```

-[Get started Vision API test preprocessing and AutoML text model generation](community/ml_ops/stage2/get_started_with_visionapi_and_automl.ipynb)

-In this tutorial, you create an `AutoML` text entity extraction model pre-existing extracted data by generating a custom import file. You deploy this mode for online prediction from a Python script using the `BigQuery`, `Vision AI`, Cloud Storage and `Vertex AI SDK` for Python. 
+[Get started Vision API test preprocessing and AutoML text model generation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_visionapi_and_automl.ipynb)
+
+```
+In this tutorial, you create an `AutoML` text entity extraction model pre-existing extracted data by generating a custom import file.

 The steps performed include:

@@ -192,9 +236,13 @@ The steps performed include:
 - Make a prediction.
 - Undeploy the `Model`.

-[Get started with Vertex AI Experiments](community/ml_ops/stage2/get_started_vertex_experiments.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Experiments` when training with `Vertex AI`.
+
+[Get started with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_experiments.ipynb)
+
+```
+Learn how to use `Vertex AI Experiments` when training with `Vertex AI`.

 The steps performed include:

@@ -215,9 +263,13 @@ The steps performed include:
    - Execute the custom job
    - Visualize the experiment results

-[AutoML Image Classfication Training with Customer Managed Encryption Keys (CMEK)](community/ml_ops/stage2/get_started_with_cmek_training.ipynb)
+```

-In this tutorial, you learn how to use a customer managed encryption key (CMEK) for `Vertex AI AutoML` training.
+
+[AutoML Image Classfication Training with Customer Managed Encryption Keys (CMEK)](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_cmek_training.ipynb)
+
+```
+Learn how to use a customer managed encryption key (CMEK) for `Vertex AI AutoML` training.

 The steps performed include:

@@ -225,9 +277,13 @@ The steps performed include:
 - Creating an image dataset with CMEK encryption.
 - Train an AutoML model with CMEK encryption.

-[Get started with Vertex AI Feature Store](community/ml_ops/stage2/get_started_vertex_feature_store.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Feature Store` when training and predicting with `Vertex AI`.
+
+[Get started with Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_feature_store.ipynb)
+
+```
+Learn how to use `Vertex AI Feature Store` when training and predicting with `Vertex AI`.

 The steps performed include:

@@ -240,9 +296,13 @@ The steps performed include:
 - Perform online serving from a `Featurestore` resource.
 - Perform batch serving from a `Featurestore` resource.

-[Get started with AutoML Training](community/ml_ops/stage2/get_started_automl_training.ipynb)
+```

-In this tutorial, you learn how to use `AutoML` for training with `Vertex AI`.
+
+[Get started with AutoML Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_automl_training.ipynb)
+
+```
+Learn how to use `AutoML` for training with `Vertex AI`.

 The steps performed include:

@@ -253,9 +313,29 @@ The steps performed include:
 - Train a text model
 - Train a video model

-[Get started with Vertex AI Training for LightGBM](community/ml_ops/stage2/get_started_vertex_training_lightgbm.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Training` for training a LightGBM custom model.
+
+[Get started with autologging using Vertex AI Experiments for XGBoost models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_vertex_experiments_autologging_xgboost.ipynb)
+
+```
+Learn how to create an experiment for training an XGBoost model, and automatically log parameters and metrics using the enclosed do-it-yourself (DIY) code.
+
+The steps performed include:
+
+- Construct the DIY autologging code.
+- Construct training package with call to autologging.
+- Train a model.
+- View the experiment
+- Delete the experiment.
+
+```
+
+
+[Get started with Vertex AI Training for LightGBM](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_lightgbm.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for training a LightGBM custom model.

 The steps performed include:

@@ -266,9 +346,26 @@ The steps performed include:
 - Test the deployment image locally.
 - Create a `Vertex AI Model` resource.

-[Get started with Vertex AI Training for Scikit-Learn](community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Training` for training a Scikit-Learn custom model.
+
+[Vertex AI Hyperparameter Tuning with R kernel](None)
+
+```
+Learn how to use `Vertex AI`, using an R kernel, for tuning hyperparameters of a R custom model.
+
+The steps performed include:
+
+- Create a custom R training script
+- Create a custom R deployment container.
+- Perform hyperparameter tuning using `Vertex AI`.
+
+```
+
+[Get started with Vertex AI Training for Scikit-Learn](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for training a Scikit-Learn custom model.

 The steps performed include:

@@ -277,9 +374,13 @@ The steps performed include:
 - Save the model artifacts to Cloud Storage using GCSFuse.
 - Create a `Vertex AI Model` resource.

-[Get started with Vertex AI Training](community/ml_ops/stage2/get_started_vertex_training.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Training` for custom models when training with `Vertex AI`.
+
+[Get started with Vertex AI Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for custom models when training with `Vertex AI`.

 The steps performed include:

@@ -288,10 +389,13 @@ The steps performed include:
 - Training using a custom training image.
 - Laying out a training package.

+```

-[Get started with Vertex AI Training for Pytorch](community/ml_ops/stage2/get_started_vertex_training_pytorch.ipynb)

-In this tutorial, you learn how to use `Vertex AI Training` for training a Pytorch custom model.
+[Get started with Vertex AI Training for PyTorch](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_training_pytorch.ipynb)
+
+```
+Learn how to use `Vertex AI Training` for training a PyTorch custom model.

 The steps performed include:

@@ -300,9 +404,31 @@ The steps performed include:
 - Save the model artifacts to Cloud Storage using GCSFuse.
 - Create a `Vertex AI Model` resource.

-[Get started with Vertex AI Distributed Training](community/ml_ops/stage2/get_started_vertex_distributed_training.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Distributed Training` for when training with `Vertex AI`.
+
+[Get started with autologging using Vertex AI Experiments for TensorFlow models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_with_vertex_experiments_autologging_tf.ipynb)
+
+```
+Learn how to create an experiment for training a TensorFlow model, and automatically log parameters and metrics using the enclosed do-it-yourself (DIY) code.
+
+The steps performed include:
+
+- Construct the DIY autologging code.
+- Construct training package for TensorFlow Sequential model with call to autologging.
+- Train a model.
+- View the experiment
+- Construct training package for TensorFlow Functional model with call to autologging.
+- Compare the experiment runs.
+- Delete the experiment.
+
+```
+
+
+[Get started with Vertex AI Distributed Training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/get_started_vertex_distributed_training.ipynb)
+
+```
+Learn how to use `Vertex AI Distributed Training` for when training with `Vertex AI`.

 The steps performed include:

@@ -312,12 +438,17 @@ The steps performed include:
 - `ReductionServer`: Train on multiple VMS and sync updates across VMS with `Vertex AI Reduction Server`.
 - `TPUTraining`: Train with multiple Cloud TPUs.

+```
+
 ### E2E Stage Example

-[Stage 2: Experimentation](mlops_experimentation.ipynb)
+[Experimentation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage2/mlops_experimentation.ipynb)

 ```
+In this tutorial, you create a MLOps stage 2: experimentation process.
+
 The steps performed include:
+
 - Review the `Dataset` resource created during stage 1.
 - Train an AutoML tabular binary classifier model in the background.
 - Build the experimental model architecture.
@@ -334,4 +465,5 @@ The steps performed include:
 - Set the evaluation results of the AutoML model as the baseline.
 - If the evaluation of the custom model is below baseline, continue to experiment with the custom model.
 - If the evaluation of the custom model is above baseline, save the model as the first best model.
+
 ```
@@ -34,9 +34,10 @@ The third stage in MLOps is formalization to develop an automated pipeline proce
 ### Get Started


-[Get started with Vertex AI Model Registry](community/ml_ops/stage3/get_started_with_model_registry.ipynb)
+[Get started with Vertex AI Model Registry](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_model_registry.ipynb)

-In this tutorial, you learn how to use `Vertex AI Model Registry` to create and register multiple versions of a model.
+```
+Learn how to use `Vertex AI Model Registry` to create and register multiple versions of a model.

 The steps performed include:

@@ -46,9 +47,13 @@ The steps performed include:
 - Deleting a model version.
 - Retraining the next model version.

-[Get started with Dataflow pipeline components](community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Dataflow`.
+
+[Get started with Dataflow pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb)
+
+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `Dataflow`.

 The steps performed include:

@@ -56,9 +61,13 @@ The steps performed include:
 - Encapsulate the Apache Beam data pipeline with a Dataflow component in a Vertex AI pipeline.
 - Execute a Vertex AI pipeline.

-[Get started with Apache Airflow and Vertex AI Pipelines](community/ml_ops/stage3/get_started_with_airflow_and_vertex_pipelines.ipynb)
+```

-In this tutorial, you learn how to use Apache Airflow with `Vertex AI Pipelines`.
+
+[Get started with Apache Airflow and Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_airflow_and_vertex_pipelines.ipynb)
+
+```
+Learn how to use Apache Airflow with `Vertex AI Pipelines`.

 The steps performed include:

@@ -67,9 +76,13 @@ The steps performed include:
 - Create a `Vertex AI Pipeline` that triggers the Airflow DAG.
 - Execute the `Vertex AI Pipeline`.

-[Get started with Kubeflow Pipelines](community/ml_ops/stage3/get_started_with_kubeflow_pipelines.ipynb)
+```

-In this tutorial, you learn how to use `Kubeflow Pipelines`(KFP).
+
+[Get started with Kubeflow Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_kubeflow_pipelines.ipynb)
+
+```
+Learn how to use `Kubeflow Pipelines`(KFP).

 The steps performed include:

@@ -80,9 +93,13 @@ The steps performed include:
 - Building sequential, parallel, multiple output components.
 - Building control flow into pipelines.

-[Get started with Vertex AI custom training pipeline components](community/ml_ops/stage3/get_started_with_custom_training_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Training`.
+
+[Get started with Vertex AI custom training pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_custom_training_pipeline_components.ipynb)
+
+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Training`.

 The steps performed include:

@@ -98,11 +115,13 @@ The steps performed include:
    - Deploying a Vertex AI custom trained model.
 - Execute a Vertex AI pipeline.

-[Get started with Dataproc Serverless pipeline components](community/ml_ops/stage3/get_started_with_dataproc_serverless_pipeline_components.ipynb)
+```


-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Dataproc Serverless` service. 
+[Get started with Dataproc Serverless pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_dataproc_serverless_pipeline_components.ipynb)

+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `Dataproc Serverless` service.

 The steps performed include:

@@ -111,9 +130,13 @@ The steps performed include:
 - `DataprocSparkSqlBatchOp` for running Spark SQL batch workloads.
 - `DataprocSparkRBatchOp` for running SparkR batch workloads.

-[Get started with Vertex AI Hyperparameter Tuning pipeline components](community/ml_ops/stage3/get_started_with_hpt_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Hyperparameter Tuning`.
+
+[Get started with Vertex AI Hyperparameter Tuning pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_hpt_pipeline_components.ipynb)
+
+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Hyperparameter Tuning`.

 The steps performed include:

@@ -125,23 +148,28 @@ The steps performed include:
      - Upload the model artifacts to a `Vertex AI Model` resource.
 - Execute a Vertex AI pipeline.

-[Get started with machine management for Vertex AI Pipelines](community/ml_ops/stage3/get_started_with_machine_management.ipynb)
+```

-In this tutorial, you convert a self-contained custom training component into a `Vertex AI CustomJob`, whereby:

-    - The training job and artifacts are trackable.
-    - Set machine resources, such as machine-type, cpu/gpu, memory, disk, etc.
+[Get started with machine management for Vertex AI Pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_machine_management.ipynb)
+
+```
+Learn how to convert a self-contained custom training component into a `Vertex AI CustomJob`, whereby:

 The steps performed in this tutorial include:

 - Create a custom component with a self-contained training job.
 - Execute pipeline using component-level settings for machine resources
 - Convert the self-contained training component into a `Vertex AI CustomJob`.
- Execute pipeline using customjob-level settings for machine resources 
+- Execute pipeline using customjob-level settings for machine resources

-[Get started with TFX pipelines](community/ml_ops/stage3/get_started_with_tfx_pipeline.ipynb)
+```

-In this tutorial, you learn how to use TensorFlow Extended (TFX) with `Vertex AI Pipelines`.
+
+[Get started with TFX pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_tfx_pipeline.ipynb)
+
+```
+Learn how to use TensorFlow Extended (TFX) with `Vertex AI Pipelines`.

 The steps performed include:

@@ -150,9 +178,28 @@ The steps performed include:
 - Execute the pipeline on Google Cloud using `Vertex AI Training`
 - Execute the pipeline using `Vertex AI Pipelines`.

-[Get started with BigQuery ML pipeline components](community/ml_ops/stage3/get_started_with_bqml_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `BigQuery ML`.
+
+[Orchestrating a workflow to train and deploy an scikit-learn model using Vertex AI Pipelines with online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_vertex_pipelines_sklearn_with_prediction.ipynb)
+
+```
+Learn how to use prebuilt components in `Vertex AI Pipelines` for training and deploying a scikit-Learn custom model, and then using `Vertex AI Prediction` to make an online prediction.
+
+The steps performed include:
+
+- Construct a scikit-learn training package.
+- Construct a pipeline to train and deploy a scikit-learn model.
+- Execute the pipeline.
+- Make an online prediction.
+
+```
+
+
+[Get started with BigQuery ML pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_bqml_pipeline_components.ipynb)
+
+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `BigQuery ML`.

 The steps performed include:

@@ -165,9 +212,28 @@ The steps performed include:
 - Execute a Vertex AI pipeline.
 - Make a prediction with the deployed Vertex AI model.

-[Get started with AutoML tabular pipeline workflows](community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.ipynb)
+```

-In this tutorial, you learn how to use `AutoML Tabular Pipeline Template` for training, exporting and tuning an AutoML tabular model.
+
+[Orchestrating a workflow to train and deploy an XGBoost model using Vertex AI Pipelines with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_vertex_pipelines_xgboost_with_experiments.ipynb)
+
+```
+Learn how to use prebuilt components in `Vertex AI Pipelines` for training and deploying a XGBoost custom model, and using `Vertex AI Experiments` to log the corresponding training parameters and metrics, from within the training package.
+
+The steps performed include:
+
+- Construct a XGBoost training package.
+  - Add tracking the experiment
+- Construct a pipeline to train and deploy a XGBoost model.
+- Execute the pipeline.
+
+```
+
+
+[Get started with AutoML tabular pipeline workflows](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.ipynb)
+
+```
+Learn how to use `AutoML Tabular Pipeline Template` for training, exporting and tuning an AutoML tabular model.

 The steps performed include:

@@ -183,9 +249,13 @@ The steps performed include:
 - Deploy exported OSS TF model.
 - Make a prediction.

-[Get started with rapid prototyping with AutoML and BigQuery ML](community/ml_ops/stage3/get_started_with_rapid_prototyping_bqml_automl.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.
+
+[Get started with rapid prototyping with AutoML and BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_rapid_prototyping_bqml_automl.ipynb)
+
+```
+Learn how to use `Vertex AI Predictions` for rapid prototyping a model.

 The steps performed include:

@@ -196,9 +266,13 @@ The steps performed include:
 - Deploying the best trained model.
 - Testing the deployed model infrastructure.

-[Get started with AutoML pipeline components](community/ml_ops/stage3/get_started_with_automl_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI AutoML`.
+
+[Get started with AutoML pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_automl_pipeline_components.ipynb)
+
+```
+Learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI AutoML`.

 The steps performed include:

@@ -208,10 +282,28 @@ The steps performed include:
    - Deploying a Vertex AI AutoML trained model.
 - Execute a Vertex AI pipeline.

+```

-[Get started with BigQuery and TFDV pipeline components](community/ml_ops/stage3/get_started_with_bq_tfdv_pipeline_components.ipynb)

-In this tutorial, you learn how to use build lightweight Python components for BigQuery and TensorFlow Data Validation.
+[Orchestrating a workflow to train and deploy an XGBoost model using Vertex AI Pipelines with online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_vertex_pipelines_xgboost_with_prediction.ipynb)
+
+```
+Learn how to use prebuilt components in `Vertex AI Pipelines` for training and deploying a XGBoost custom model, and then using `Vertex AI Prediction` to make an online prediction.
+
+The steps performed include:
+
+- Construct a XGBoost training package.
+- Construct a pipeline to train and deploy a XGBoost model.
+- Execute the pipeline.
+- Make an online prediction.
+
+```
+
+
+[Get started with BigQuery and TFDV pipeline components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/get_started_with_bq_tfdv_pipeline_components.ipynb)
+
+```
+Learn how to use build lightweight Python components for BigQuery and TensorFlow Data Validation.

 The steps performed include:

@@ -219,22 +311,28 @@ The steps performed include:
 - Build and execute a pipeline component for generating TFDV statistics and schema from a Vertex AI Tabular Dataset.
 - Execute a Vertex AI pipeline.

+```
+
 ### E2E Stage Example

-[Stage 3: Formalization](mlops_formalization.ipynb)
+[Formalization](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage3/mlops_formalization.ipynb)

 ```
+In this tutorial, you create a MLOps stage 3: formalization process.
+
 The steps performed include:
+
 - Obtain resources from the experimentation stage.
    - Baseline model.
    - Dataset schema/statistics for baseline model.
 - Formalize a data preprocessing pipeline.
    - Extract columns/rows from BigQuery table to local BigQuery table.
-    - Use Tensorflow Data Validation library to determine statistics, schema, and features.
+    - Use TensorFlow Data Validation library to determine statistics, schema, and features.
    - Use Dataflow to preprocess the data.
    - Create a Vertex AI Dataset.
 - Formalize a build model architecture pipeline.
    - Create the Vertex AI Model base model.
 - Formalize a training pipeline.
+
 ```

@@ -43,191 +43,104 @@ This stage may be done entirely by MLOps. We recommend:
 ### Get Started


-[Get started with Vertex AI Model Registry](community/ml_ops/stage3/get_started_with_model_registry.ipynb)
+[Get started with Vertex ML Metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage4/get_started_with_vertex_ml_metadata.ipynb)

-In this tutorial, you learn how to use `Vertex AI Model Registry` to create and register multiple versions of a model.
+```
+Learn how to use `Vertex ML Metadata`.

 The steps performed include:

- Create and register a first version of a model to `Vertex AI Model Registry`.
- Create and register a second version of a model to `Vertex AI Model Registry`.
- Updating the model version which is the default (blessed).
- Deleting a model version.
- Retraining the next model version.
+- Create a `Metadatastore` resource.
+- Create (record)/List an `Artifact`, with artifacts and metadata.
+- Create (record)/List an `Execution`.
+- Create (record)/List a `Context`.
+- Add `Artifact` to `Execution` as events.
+- Add `Execution` and `Artifact` into the `Context`
+- Delete `Artifact`, `Execution` and `Context`.
+- Create and run a `Vertex AI Pipeline` ML workflow to train and deploy a scikit-learn model.
+    - Create custom pipeline components that generate artifacts and metadata.
+    - Compare Vertex AI Pipelines runs.
+    - Trace the lineage for pipeline-generated artifacts.
+    - Query your pipeline run metadata.

-[Get started with Dataflow pipeline components](community/ml_ops/stage3/get_started_with_dataflow_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Dataflow`.
+
+[Get started with Google Artifact Registry](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage4/get_started_with_google_artifact_registry.ipynb)
+
+```
+Learn how to use `Google Artifact Registry`.

 The steps performed include:

- Build an Apache Beam data pipeline.
- Encapsulate the Apache Beam data pipeline with a Dataflow component in a Vertex AI pipeline.
- Execute a Vertex AI pipeline.
+- Creating a private Docker repository.
+- Tagging a container image, specific to the private Docker repository.
+- Pushing a container image to the private Docker repository.
+- Pulling a container image from the private Docker repository.
+- Deleting a private Docker repository.

-[Get started with Apache Airflow and Vertex AI Pipelines](community/ml_ops/stage3/get_started_with_airflow_and_vertex_pipelines.ipynb)
+```

-In this tutorial, you learn how to use Apache Airflow with `Vertex AI Pipelines`.
+
+[Get started with Vertex AI Model Evaluation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage4/get_started_with_model_evaluation.ipynb)
+
+```
+Learn how to use `Vertex AI Model Evaluation`.

 The steps performed include:

- Create Cloud Composer environment.
- Upload Airflow DAG to Composer environment that performs data processing -- i.e., creates a BigQuery table from a CSV file.
- Create a `Vertex AI Pipeline` that triggers the Airflow DAG.
- Execute the `Vertex AI Pipeline`.
+```

-[Get started with Kubeflow Pipelines](community/ml_ops/stage3/get_started_with_kubeflow_pipelines.ipynb)

-In this tutorial, you learn how to use `Kubeflow Pipelines`(KFP).
+[Get started with Vertex Explainable AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage4/get_started_with_vertex_xai.ipynb)
+
+```
+Learn how to use `Vertex AI Explainable AI`.

 The steps performed include:

- Building KFP lightweight Python function components.
- Assembling and compiling KFP components into a pipeline.
- Executing a KFP pipeline using Vertex AI Pipelines.
- Loading component and pipeline definitions from a source code repository.
- Building sequential, parallel, multiple output components.
- Building control flow into pipelines.
+- Train an AutoML tabular model.
+    - Do a batch prediction with explanations.
+    - Do an online prediction with explanations.
+- Train an custom TensorFlow tabular model.
+    - Manually set configuration metadata.
+    - Do a batch prediction with explanations.
+    - Do an online prediction with explanations.
+    - Automatically set configuration metadata.
+- Train an custom TensorFlow image model.
+    - Manually set configuration metadata.
+    - Do a batch prediction with explanations.
+    - Do an online prediction with explanations.
+- Train an custom XGBoost tabular model.
+    - Manually set configuration metadata.
+    - Do an online prediction with explanations.
+- Train an custom scikit-learn tabular model.
+    - Manually set configuration metadata.
+    - Do an online prediction with explanations.

-[Get started with Vertex AI custom training pipeline components](community/ml_ops/stage3/get_started_with_custom_training_pipeline_components.ipynb)
+```

-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Training`.
+
+[Get started with AutoML Training and ML Metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage4/get_started_with_vertex_ml_metadata_and_automl.ipynb)
+
+```
+Learn how to use `AutoML` for training and assemble the corresponding artifact linkage for `Vertex ML Metadata`.

 The steps performed include:

- Construct a pipeline for:
-    - Training a Vertex AI custom trained model.
-    - Test the serving binary with a batch prediction job.
-    - Deploying a Vertex AI custom trained model.
- Execute a Vertex AI pipeline.
- Construct a pipeline for:
-     - Construct a custom training component.
-     - Convert custom training component to CustomTrainingJobOp.
-     - Training a Vertex AI custom trained model using the converted component.
-    - Deploying a Vertex AI custom trained model.
- Execute a Vertex AI pipeline.
+- Create a `Dataset` resource.
+- Create a corresponding `google.VertexDataset` artifact.
+- Train a model using `AutoML`.
+- Create a corresponding `google.VertexModel` artifact.
+- Create an `Endpoint` resource.
+- Create a corresponding `google.Endpoint` artifact.
+- Deploy the train model to the `Endpoint`.
+- Create an execution and context for the `AutoML` training job and deployment.
+- Add the corresponding artifacts and context to the execution.
+- Add artifact links (event) to the execution.
+- Display the execution graph.

-[Get started with Dataproc Serverless pipeline components](community/ml_ops/stage3/get_started_with_dataproc_serverless_pipeline_components.ipynb)
-
-
-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Dataproc Serverless` service. 
-
-
-The steps performed include:
-
- `DataprocPySparkBatchOp` for running PySpark batch workloads.
- `DataprocSparkBatchOp` for running Spark batch workloads.
- `DataprocSparkSqlBatchOp` for running Spark SQL batch workloads.
- `DataprocSparkRBatchOp` for running SparkR batch workloads.
-
-[Get started with Vertex AI Hyperparameter Tuning pipeline components](community/ml_ops/stage3/get_started_with_hpt_pipeline_components.ipynb)
-
-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI Hyperparameter Tuning`.
-
-The steps performed include:
-
- Construct a pipeline for:
-    - Hyperparameter tune/train a custom model.
-    - Retrieve the tuned hyperparameter values and metrics to optimize.
-    - If the metrics exceed a specified threshold.
-      - Get the location of the model artifacts for the best tuned model.
-      - Upload the model artifacts to a `Vertex AI Model` resource.
- Execute a Vertex AI pipeline.
-
-[Get started with machine management for Vertex AI Pipelines](community/ml_ops/stage3/get_started_with_machine_management.ipynb)
-
-In this tutorial, you convert a self-contained custom training component into a `Vertex AI CustomJob`, whereby:
-
-    - The training job and artifacts are trackable.
-    - Set machine resources, such as machine-type, cpu/gpu, memory, disk, etc.
-
-The steps performed in this tutorial include:
-
- Create a custom component with a self-contained training job.
- Execute pipeline using component-level settings for machine resources
- Convert the self-contained training component into a `Vertex AI CustomJob`.
- Execute pipeline using customjob-level settings for machine resources 
-
-[Get started with TFX pipelines](community/ml_ops/stage3/get_started_with_tfx_pipeline.ipynb)
-
-In this tutorial, you learn how to use TensorFlow Extended (TFX) with `Vertex AI Pipelines`.
-
-The steps performed include:
-
- Create a TFX e2e pipeline.
- Execute the pipeline locally.
- Execute the pipeline on Google Cloud using `Vertex AI Training`
- Execute the pipeline using `Vertex AI Pipelines`.
-
-[Get started with BigQuery ML pipeline components](community/ml_ops/stage3/get_started_with_bqml_pipeline_components.ipynb)
-
-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `BigQuery ML`.
-
-The steps performed include:
-
- Construct a pipeline for:
-    - Training BigQuery ML model.
-    - Evaluating the BigQuery ML model.
-    - Exporting the BigQuery ML model.
-    - Importing the BigQuery ML model to a Vertex AI model.
-    - Deploy the Vertex AI model.
- Execute a Vertex AI pipeline.
- Make a prediction with the deployed Vertex AI model.
-
-[Get started with AutoML tabular pipeline workflows](community/ml_ops/stage3/get_started_with_automl_tabular_pipeline_workflow.ipynb)
-
-In this tutorial, you learn how to use `AutoML Tabular Pipeline Template` for training, exporting and tuning an AutoML tabular model.
-
-The steps performed include:
-
- Define training specification.
-    - Dataset specification
-    - Hyperparameter overide specification
-    - machine specifications
- Construct tabular workflow pipeline.
- Compile and execute pipeline.
- View evaluation metrics artifact.
- Export AutoML model as an OSS TF model.
- Create `Endpoint` resource.
- Deploy exported OSS TF model.
- Make a prediction.
-
-[Get started with rapid prototyping with AutoML and BigQuery ML](community/ml_ops/stage3/get_started_with_rapid_prototyping_bqml_automl.ipynb)
-
-In this tutorial, you learn how to use `Vertex AI Predictions` for rapid prototyping a model.
-
-The steps performed include:
-
- Creating a BigQuery and Vertex AI training dataset.
- Training a BigQuery ML and AutoML model.
- Extracting evaluation metrics from the BigQueryML and AutoML models.
- Selecting the best trained model.
- Deploying the best trained model.
- Testing the deployed model infrastructure.
-
-[Get started with AutoML pipeline components](community/ml_ops/stage3/get_started_with_automl_pipeline_components.ipynb)
-
-In this tutorial, you learn how to use prebuilt `Google Cloud Pipeline Components` for `Vertex AI AutoML`.
-
-The steps performed include:
-
- Construct a pipeline for:
-    - Training a Vertex AI AutoML trained model.
-    - Test the serving binary with a batch prediction job.
-    - Deploying a Vertex AI AutoML trained model.
- Execute a Vertex AI pipeline.
-
-
-[Get started with BigQuery and TFDV pipeline components](community/ml_ops/stage3/get_started_with_bq_tfdv_pipeline_components.ipynb)
-
-In this tutorial, you learn how to use build lightweight Python components for BigQuery and TensorFlow Data Validation.
-
-The steps performed include:
-
- Build and execute a pipeline component for creating a Vertex AI Tabular Dataset from a BigQuery table.
- Build and execute a pipeline component for generating TFDV statistics and schema from a Vertex AI Tabular Dataset.
- Execute a Vertex AI pipeline.
+```

 ### E2E Stage Example

-Stage 4: Evaluation
@@ -25,9 +25,10 @@ The fifth stage in MLOps is deployment to production of the blessed model, which
 ### Get Started


-[Get started with Vertex AI Endpoints](community/ml_ops/stage5/get_started_with_vertex_endpoints.ipynb)
+[Get started with Vertex AI Endpoints](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage5/get_started_with_vertex_endpoints.ipynb)

-In this tutorial, you learn how to use `Vertex AI Endpoint` resources.
+```
+Learn how to use `Vertex AI Endpoint` resources.

 The steps performed include:

@@ -46,9 +47,13 @@ The steps performed include:
 - In pipeline: Create an `Endpoint` resource and deploy an existing `Model` resource to the `Endpoint` resource.
 - In pipeline: Deploy an existing `Model` resource to an existing `Endpoint` resource.

-[Get started with Vertex AI Endpoint and shared VM](community/ml_ops/stage5/get_started_with_vertex_endpoint_and_shared_vm.ipynb)
+```

-In this tutorial, you learn how to use deployment resource pools for deploying models. A deployment resouce pool provides one with the ability to co-host more than one model on the same (shared) VM.
+
+[Get started with Vertex AI Endpoint and shared VM](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage5/get_started_with_vertex_endpoint_and_shared_vm.ipynb)
+
+```
+Learn how to use deployment resource pools for deploying models.

 The steps performed include:

@@ -62,9 +67,13 @@ The steps performed include:
 - Make a prediction request with first deployed model (model A).
 - Make a prediction request with second deployed model (model B).

-[Get started with configuring autoscaling for Vertex AI Endpoint deployment](community/ml_ops/stage5/get_started_with_autoscaling.ipynb)
+```

-In this tutorial, you learn how to use fine-tune control auto-scaling configuration when deploying a `Model` resource to an `Endpoint` resource.
+
+[Get started with configuring autoscaling for Vertex AI Endpoint deployment](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage5/get_started_with_autoscaling.ipynb)
+
+```
+Learn how to use fine-tune control auto-scaling configuration when deploying a `Model` resource to an `Endpoint` resource.

 The steps performed include:

@@ -78,9 +87,13 @@ The steps performed include:
 - Fine-tune scaling thresholds for GPU utilization.
 - Deploy mix of CPU and GPU model instances with auto-scaling to an `Endpoint` resource.

-[Get started with Vertex AI Private Endpoints](community/ml_ops/stage5/get_started_with_vertex_private_endpoints.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Private Endpoint` resources.
+
+[Get started with Vertex AI Private Endpoints](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage5/get_started_with_vertex_private_endpoints.ipynb)
+
+```
+Learn how to use `Vertex AI Private Endpoint` resources.

 The steps performed include:

@@ -89,8 +102,5 @@ The steps performed include:
 - Configuring the serving binary of a `Model` resource for deployment to a `Private Endpoint` resource.
 - Deploying a `Model` resource to a `Private Endpoint` resource.
 - Send a prediction request to a `Private Endpoint`
- Enable two additional APIs: Service Networking and Cloud DNS.
- Add Compute Admin Network role to your (default) service account.
- Issue two gcloud commands to setup the VPC peering for your service account.
- There is *currently* no SDK support yet, so private endpoint is created with GAPIC client and has an extra argument for the peering network.
- To send a request, you can't use SDK/GAPIC since they do a HTTP internet request. Instead, you use curl to send a peer-to-peer request.
+
+```
@@ -30,19 +30,23 @@ This stage may be done entirely by MLOps. We recommend:
 ### Get Started


-[Get started with Vertex AI Batch Prediction for AutoML image models](community/ml_ops/stage6/get_started_with_automl_image_model_batch.ipynb)
+[Get started with Vertex AI Batch Prediction for AutoML image models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_image_model_batch.ipynb)

-In this tutorial, you create an AutoML image classification model from a Python script, and then do a batch prediction using the Vertex AI SDK. 
+```
+Learn how to create an AutoML image classification model from a Python script, and then do a batch prediction using the Vertex AI SDK.

 The steps performed include:

 - Create a Vertex `Dataset` resource.
 - Train an `AutoML` image classification model.
 - Make a batch prediction with JSONL input.
+```

-[Get started with Vertex AI Matching Engine and Swivel builtin algorithm](community/ml_ops/stage6/get_started_with_matching_engine_swivel.ipynb)

-In this notebook, you learn how to train custom embeddings using Vertex AI Pipelines and subsequently train and deploy a matching engine index using the embeddings.
+[Get started with Vertex AI Matching Engine and Swivel builtin algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_matching_engine_swivel.ipynb)
+
+```
+Learn how to train custom embeddings using Vertex AI Pipelines and subsequently train and deploy a matching engine index using the embeddings.

 The steps performed include:

@@ -54,9 +58,13 @@ The steps performed include:
 6. Deploy the `Matching Engine Index` to a `Index Endpoint`.
 7. Make a matching engine prediction request.

-[Get started with Vertex AI Matching Engine](community/ml_ops/stage6/get_started_with_matching_engine.ipynb)
+```

-In this notebook, you learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes. 
+
+[Get started with Vertex AI Matching Engine](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_matching_engine.ipynb)
+
+```
+Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes.

 The steps performed include:

@@ -67,10 +75,13 @@ The steps performed include:
 - Deploy brute force Index.
 - Perform calibration between ANN and brute force index.

-[Get started with Vertex AI Matching Engine and Two Towers builtin algorithm](community/ml_ops/stage6/get_started_with_matching_engine_twotowers.ipynb)
+```


-In this notebook, you learn how to use the `Two-Tower` builtin algorithms for generating embeddings for a dataset, for use with generating an `Matching Engine Index`, with the `Vertex AI Matching Engine` service.
+[Get started with Vertex AI Matching Engine and Two Towers builtin algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_matching_engine_twotowers.ipynb)
+
+```
+Learn how to use the `Two-Tower` builtin algorithms for generating embeddings for a dataset, for use with generating an `Matching Engine Index`, with the `Vertex AI Matching Engine` service.

 The steps performed include:

@@ -83,9 +94,30 @@ The steps performed include:
 7. Deploy the `Matching Engine Index` to a `Index Endpoint`.
 8. Make a matching engine prediction request.

-[Get started with Vertex AI Batch Prediction for custom tabular models](community/ml_ops/stage6/get_started_with_custom_tabular_model_batch.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Batch Prediction`  with a custom tabular model.
+
+[Get started with TensorFlow Serving with Vertex AI Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_tf_serving_tabular.ipynb)
+
+```
+Learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` resource with `TensorFlow Serving` serving binary.
+
+The steps performed include:
+
+- Download a pretrained TensorFlow tabular model.
+- Upload the TensorFlow model as a `Vertex AI Model` resource.
+- Creating an `Endpoint` resource.
+- Deploying the `Model` resource to an `Endpoint` resource with `TensorFlow Serving` serving binary.
+- Make an online prediction to the `Model` resource instance deployed to the `Endpoint` resource.
+- Make a batch prediction to the `Model` resource instance.
+
+```
+
+
+[Get started with Vertex AI Batch Prediction for custom tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_custom_tabular_model_batch.ipynb)
+
+```
+Learn how to use `Vertex AI Batch Prediction`  with a custom tabular model.

 The steps performed include:

@@ -93,10 +125,13 @@ The steps performed include:
 - Make batch prediction to the `Model` resource, in JSONL format.
 - Make batch prediction to the `Model` resource, in CSV format.
 - Make batch prediction to the `Model` resource, in BigQuery format.
+```

-[Get started with Optimized TensorFlow Enterprise container with Vertex AI Prediction / text models](community/ml_ops/stage6/get_started_with_optimized_tfe_bert.ipynb)

-In this tutorial, you learn how to use `TensorFlow Enterprise Optimized` container for TensorFlow models deployed to a `Vertex AI Endpoint` resource.
+[Get started with Optimized TensorFlow Enterprise container with Vertex AI Prediction / text models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_optimized_tfe_bert.ipynb)
+
+```
+Learn how to use `TensorFlow Enterprise Optimized` container for TensorFlow models deployed to a `Vertex AI Endpoint` resource.

 The steps performed include:

@@ -113,9 +148,13 @@ The steps performed include:
 - Deploy the `Model` resoure with then `TensorFlow Enterprise Optimized` to the `Private Endpoint` resource.
 - Make an online prediction request to the `Private Endpoint` resource.

-[Get started with Vertex AI Batch Prediction and Explainable AI for AutoML tabular models](community/ml_ops/stage6/get_started_with_automl_tabular_model_batch.ipynb)
+```

-In this tutorial, you create an AutoML tabular binary classification model from a Python script, and then do a batch prediction with Explainable AI using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.
+
+[Get started with Vertex AI Batch Prediction and Explainable AI for AutoML tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_tabular_model_batch.ipynb)
+
+```
+In this tutorial, you create an AutoML tabular binary classification model from a Python script, and then do a batch prediction with Explainable AI using the Vertex AI SDK.

 The steps performed include:

@@ -126,10 +165,13 @@ The steps performed include:
 - Make a batch prediction with JSONL list input.
 - Make a batch prediction with BigQuery table input.
 - Make a batch prediction with explanations.
+```

-[Get started with re-importing AutoML tabular models](community/ml_ops/stage6/get_started_with_automl_tabular_exported_deploy.ipynb)

-In this tutorial, you learn how to use `AutoML Tabular` for re-importing exported model artifacts as a `Model` resource. This is useful for example, if one wants to move the exported model across projects.
+[Get started with re-importing AutoML tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_tabular_exported_deploy.ipynb)
+
+```
+Learn how to use `AutoML Tabular` for re-importing exported model artifacts as a `Model` resource.

 The steps performed include:

@@ -138,19 +180,44 @@ The steps performed include:
 - Deploy the `Model` resource to the `Endpoint` resource.
 - Make a prediction.

-[Get started with Vertex AI Batch Prediction for AutoML text models](community/ml_ops/stage6/get_started_with_automl_text_model_batch.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Batch Prediction`  with a `AutoML` text model.
+
+[Get started with Vertex AI Online Prediction for XGBoost custom models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_xgboost_model_online.ipynb)
+
+```
+In this tutorial, you deploy an XGBoost model, and then do an online prediction using the Vertex AI SDK.
+
+The steps performed include:
+
+- Upload an XGBoost model as a Vertex AI Model resource.
+- Deploy the model to a Vertex AI Endpoint resource.
+- Make an online prediction.
+- Construct a Vertex AI Pipeline:
+    - Upload an XGBoost model as a Vertex AI Model resource.
+    - Deploy the model to a Vertex AI Endpoint resource.
+- Make an online prediction
+
+```
+
+
+[Get started with Vertex AI Batch Prediction for AutoML text models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_text_model_batch.ipynb)
+
+```
+Learn how to use `Vertex AI Batch Prediction`  with a `AutoML` text model.

 The steps performed include:

 - Create a Vertex `Dataset` resource.
 - Train an `AutoML` model.
 - Make a batch prediction with JSONL input
+```

-[Get started with Vertex AI Prediction for AutoML text models](community/ml_ops/stage6/get_started_with_automl_text_model_online.ipynb)

-In this tutorial, you learn how to use `Vertex AI Prediction`  with a `AutoML` text model.
+[Get started with Vertex AI Prediction for AutoML text models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_text_model_online.ipynb)
+
+```
+Learn how to use `Vertex AI Prediction`  with a `AutoML` text model.

 The steps performed include:

@@ -159,9 +226,13 @@ The steps performed include:
 - Deploy the model to an `Endpoint` resource.
 - Make an online prediction.

-[Get started with TensorFlow serving functions with Vertex AI Raw Prediction](community/ml_ops/stage6/get_started_with_raw_predict.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Raw Prediction` on a `Vertex AI Endpoint` resource.
+
+[Get started with TensorFlow serving functions with Vertex AI Raw Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_raw_predict.ipynb)
+
+```
+Learn how to use `Vertex AI Raw Prediction` on a `Vertex AI Endpoint` resource.

 The steps performed include:

@@ -171,9 +242,13 @@ The steps performed include:
 - Deploying the `Model` resource to an `Endpoint` resource.
 - Make an online raw prediction to the `Model` resource instance deployed to the `Endpoint` resource.

-[Get started with TensorFlow serving functions with Vertex AI Prediction](community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` resource with a serving function.
+
+[Get started with TensorFlow serving functions with Vertex AI Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_tf_serving_function.ipynb)
+
+```
+Learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` resource with a serving function.

 The steps performed include:

@@ -184,13 +259,17 @@ The steps performed include:
 - Deploying the `Model` resource to an `Endpoint` resource.
 - Make an online prediction to the `Model` resource instance deployed to the `Endpoint` resource.

-[Get started with Vertex Explainable AI using custom deployment container](community/ml_ops/stage6/get_started_with_xai_and_custom_server.ipynb)
+```

-In this tutorial, you learn to build a custom container to serve a PyTorch model on `Vertex AI Endpoint`. 
+
+[Get started with Vertex Explainable AI using custom deployment container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_xai_and_custom_server.ipynb)
+
+```
+Learn to build a custom container to serve a PyTorch model on `Vertex AI Endpoint`.

 The steps performed include:

- Locally train a Pytorch tabular classifier.
+- Locally train a PyTorch tabular classifier.
 - Locally test the trained model.
 - Build a HTTP server using FastAPI.
 - Create a custom serving container with the trained model and FastAPI server.
@@ -201,9 +280,13 @@ The steps performed include:
 - Make a prediction request to the deployed custom serving container.
 - Make an explanation request to the deployed custom serving container.

-[Get started with Vertex AI Online Prediction for AutoML image models](community/ml_ops/stage6/get_started_with_automl_image_model_online.ipynb)
+```

-In this tutorial, you create an AutoML image classification model from a Python script, and then do an online prediction using the Vertex AI SDK. 
+
+[Get started with Vertex AI Online Prediction for AutoML image models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_image_model_online.ipynb)
+
+```
+In this tutorial, you create an AutoML image classification model from a Python script, and then do an online prediction using the Vertex AI SDK.

 The steps performed include:

@@ -211,9 +294,13 @@ The steps performed include:
 - Train an `AutoML` image classification model.
 - Make an online prediction.

-[Get started with FastAPI with Vertex AI Prediction](community/ml_ops/stage6/get_started_with_fastapi.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` with a custom serving binary using `FastAPI`.
+
+[Get started with FastAPI with Vertex AI Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_fastapi.ipynb)
+
+```
+Learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` with a custom serving binary using `FastAPI`.

 The steps performed include:

@@ -224,9 +311,13 @@ The steps performed include:
 - Deploying the `Model` resource to an `Endpoint` resource with `FastAPI` custom serving binary.
 - Make an online prediction to the `Model` resource instance deployed to the `Endpoint` resource.

-[Get started with Vertex AI Online Prediction for AutoML tabular models](community/ml_ops/stage6/get_started_with_automl_tabular_model_online.ipynb)
+```

-In this tutorial, you create an AutoML tabular binary classification model from a Python script, and then do an online prediction using the Vertex AI SDK. 
+
+[Get started with Vertex AI Online Prediction for AutoML tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_tabular_model_online.ipynb)
+
+```
+In this tutorial, you create an AutoML tabular binary classification model from a Python script, and then do an online prediction using the Vertex AI SDK.

 The steps performed include:

@@ -236,9 +327,13 @@ The steps performed include:
 - Make an online prediction.
 - Make an online prediction with explanations.

-[Get started with TensorFlow Serving with Vertex AI Prediction](community/ml_ops/stage6/get_started_with_tf_serving.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` resource with `TensorFlow Serving` serving binary.
+
+[Get started with TensorFlow Serving with Vertex AI Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_tf_serving.ipynb)
+
+```
+Learn how to use `Vertex AI Prediction` on a `Vertex AI Endpoint` resource with `TensorFlow Serving` serving binary.

 The steps performed include:

@@ -250,9 +345,13 @@ The steps performed include:
 - Make an online prediction to the `Model` resource instance deployed to the `Endpoint` resource.
 - Make a batch prediction to the `Model` resource instance.

-[Get started with Custom Prediction Routine (CPR)](community/ml_ops/stage6/get_started_with_cpr.ipynb)
+```

-In this tutorial, you learn how to use Custom Prediction Routine (CPR) for `Vertex AI Predictions`.
+
+[Get started with Custom Prediction Routine (CPR)](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_cpr.ipynb)
+
+```
+Learn how to use Custom Prediction Routine (CPR) for `Vertex AI Predictions`.

 The steps performed include:

@@ -278,19 +377,26 @@ The steps performed include:
    - Upload and deploy the model serving container to Vertex AI Endpoint.
    - Make a prediction request.

-[Get started with Vertex AI Batch Prediction for custom text models](community/ml_ops/stage6/get_started_with_custom_text_model_batch.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Batch Prediction`  with a custom text model.
+
+[Get started with Vertex AI Batch Prediction for custom text models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_custom_text_model_batch.ipynb)
+
+```
+Learn how to use `Vertex AI Batch Prediction`  with a custom text model.

 The steps performed include:

 - Download a pretrained TensorFlow RNN model.
 - Upload the pretrained model as a `Vertex AI Model` resource.
 - Make batch prediction to the `Model` resource, in JSONL format.
+```

-[Get started with NVIDIA Triton server](community/ml_ops/stage6/get_started_with_nvidia_triton_serving.ipynb)

-In this tutorial, you deploy a container running Nvidia Triton Server with a `Vertex AI Model` resource to a `Vertex AI Endpoint` for making online predictions.
+[Get started with NVIDIA Triton server](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_nvidia_triton_serving.ipynb)
+
+```
+Learn how to deploy a container running Nvidia Triton Server with a `Vertex AI Model` resource to a `Vertex AI Endpoint` for making online predictions.

 The steps performed in this tutorial include:

@@ -302,9 +408,13 @@ The steps performed in this tutorial include:
 - Make a prediction request
 - Undeploy the `Model` resource and delete the `Endpoint`

-[Get started with Vertex AI Batch Prediction for custom image models](community/ml_ops/stage6/get_started_with_custom_image_model_batch.ipynb)
+```

-In this tutorial, you learn how to use `Vertex AI Batch Prediction`  with a custom image model.
+
+[Get started with Vertex AI Batch Prediction for custom image models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_custom_image_model_batch.ipynb)
+
+```
+Learn how to use `Vertex AI Batch Prediction`  with a custom image model.

 The steps performed include:

@@ -314,13 +424,17 @@ The steps performed include:
 - Create a serving function to receive compressed image data, and output decomopressed preprocessed data for the model input.
 - Upload the TensorFlow Hub model and serving function as a `Vertex AI Model` resource.
 - Make batch prediction with compressed image data to the `Model` resource, in File-List format.
+```

-[Get started with Vertex AI Batch Prediction for AutoML video models](community/ml_ops/stage6/get_started_with_automl_video_model_batch.ipynb)

-In this tutorial, you learn how to use `Vertex AI Batch Prediction`  with a `AutoML` video model.
+[Get started with Vertex AI Batch Prediction for AutoML video models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_video_model_batch.ipynb)
+
+```
+Learn how to use `Vertex AI Batch Prediction`  with a `AutoML` video model.

 The steps performed include:

 - Create a Vertex `Dataset` resource.
 - Train an `AutoML` model.
- Make a batch prediction with JSONL input.
+- Make a batch prediction with JSONL input
+```
@@ -35,9 +35,28 @@ This stage may be done entirely by MLOps. We recommend:

 ### Get Started

-[Vertex AI Model Monitoring for custom tabular models with TensorFlow Serving container](community/ml_ops/stage7/get_started_with_model_monitoring_custom_tf_serving.ipynb)
+[Vertex AI Model Monitoring for XGBoost models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage7/get_started_with_model_monitoring_xgboost.ipynb)

-In this notebook, you learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for custom tabular models, using a custom deployment container. 
+```
+Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests for XGBoost models.
+
+The steps performed include:
+
+- Download a pre-trained XGBoost model.
+- Upload the pre-trained model as a `Model` resource.
+- Deploy the `Model` resource to the `Endpoint` resource.
+- Configure the `Endpoint` resource for model monitoring:
+  - drift detection only -- no access to training data.
+  - predefine the input schema to map feature alias names to the unnamed array input to the model.
+- Generate synthetic prediction requests for drift.
+
+```
+
+
+[Vertex AI Model Monitoring for custom tabular models with TensorFlow Serving container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage7/get_started_with_model_monitoring_custom_tf_serving.ipynb)
+
+```
+Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for custom tabular models, using a custom deployment container.

 The steps performed include:

@@ -50,11 +69,13 @@ The steps performed include:
 - Generate synthetic prediction requests for drift.
 - Wait for email alert notification.

+```


-[Vertex AI Model Monitoring for AutoML tabular models](community/ml_ops/stage7/get_started_with_model_monitoring_automl.ipynb)
+[Vertex AI Model Monitoring for AutoML tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage7/get_started_with_model_monitoring_automl.ipynb)

-In this notebook, you learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for AutoML tabular models.
+```
+Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for AutoML tabular models.

 The steps performed include:

@@ -66,10 +87,13 @@ The steps performed include:
 - Generate synthetic prediction requests for drift.
 - Wait for email alert notification.

+```

-[Vertex AI Model Monitoring for custom tabular models](community/ml_ops/stage7/get_started_with_model_monitoring_custom.ipynb)

-In this notebook, you learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for custom tabular models.
+[Vertex AI Model Monitoring for custom tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage7/get_started_with_model_monitoring_custom.ipynb)
+
+```
+Learn to use the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests, for custom tabular models.

 The steps performed include:

@@ -82,11 +106,13 @@ The steps performed include:
 - Generate synthetic prediction requests for drift.
 - Wait for email alert notification.

+```


-[Vertex AI Model Monitoring for setup for tabular models](community/ml_ops/stage7/get_started_with_model_monitoring_setup.ipynb)
+[Vertex AI Model Monitoring for setup for tabular models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage7/get_started_with_model_monitoring_setup.ipynb)

-In this notebook, you learn to setup the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests.
+```
+Learn to setup the `Vertex AI Model Monitoring` service to detect feature skew and drift in the input predict requests.

 The steps performed include:

@@ -100,3 +126,5 @@ The steps performed include:
 - List, pause, resume and delete monitoring jobs.
 - Restart monitoring job with predefined `input schema`.
 - View logged monitored data.
+
+```
@@ -29,7 +29,7 @@
        "id": "JAPoU8Sm5E6e"
      },
      "source": [
-        "# Model Versioning with Vertex AI Model Registry\n",
+        "# Model Management with Vertex AI Model Registry\n",
        "\n",
        "\n",
        "<table align=\"left\">\n",
@@ -1292,10 +1292,10 @@
        "      pandas \\\n",
        "      python \\\n",
        "      pyspark \\\n",
-        "      findspark\n",
+        "      findspark \n",
        "\n",
        "# Use conda to install spark-nlp\n",
-        "RUN ${CONDA_HOME}/bin/conda install -n base -c johnsnowlabs spark-nlp\n",
+        "RUN ${CONDA_HOME}/bin/conda install -n base -c johnsnowlabs 'spark-nlp=4.0.2'\n",
        "\n",
        "# Add lemma dictionary\n",
        "# ENV CONFIG_DIR='/home/app/build'\n",
@@ -1425,7 +1425,7 @@
        "import sparknlp\n",
        "from sparknlp.base import *\n",
        "from sparknlp.annotator import *\n",
-        "from pyspark.ml.feature import CountVectorizer\n",
+        "from pyspark.ml.feature import CountVectorizer, SQLTransformer\n",
        "from pyspark.ml import Pipeline\n",
        "\n",
        "# Variables ------------------------------------------------------------------------------------------------------------\n",
@@ -1473,7 +1473,7 @@
        "    Returns:\n",
        "        preliminary_steps: The preliminary steps for the preprocessing.\n",
        "    '''\n",
-        "\n",
+        "    \n",
        "    document_assembler = DocumentAssembler().setInputCol(\"text\").setOutputCol(\"document\").setCleanupMode('shrink_full')\n",
        "    sentence_detector = SentenceDetector().setInputCols(\"document\").setOutputCol(\"sentence\")\n",
        "    tokenizer = Tokenizer().setInputCols(\"sentence\").setOutputCol(\"token\")\n",
@@ -1512,6 +1512,16 @@
        "    feature_extraction_steps = [count_vectorizer]\n",
        "    return feature_extraction_steps\n",
        "\n",
+        "def build_postprocessing_steps():\n",
+        "    '''\n",
+        "    This function builds the postprocessing steps.\n",
+        "    Returns:\n",
+        "        target_conversion_step: The target conversion step.\n",
+        "    '''\n",
+        "\n",
+        "    sql_transformer = SQLTransformer(statement=\"SELECT CASE WHEN (category != 'business') THEN 'other' ELSE category END AS category, text, lemma_features, features  FROM __THIS__\")\n",
+        "    build_postprocessing_steps = [sql_transformer]\n",
+        "    return build_postprocessing_steps\n",
        "\n",
        "def read_data(spark_session, data_schema, input_dir):\n",
        "    '''\n",
@@ -1599,7 +1609,8 @@
        "    preliminary_steps = build_preliminary_steps()\n",
        "    common_preprocess_steps = build_common_preprocess_steps(lemma_uri)\n",
        "    feature_extraction_steps = build_feature_extraction_steps()\n",
-        "    pipeline = Pipeline(stages=preliminary_steps + common_preprocess_steps + feature_extraction_steps)\n",
+        "    postprocessing_steps = build_postprocessing_steps()\n",
+        "    pipeline = Pipeline(stages=preliminary_steps + common_preprocess_steps + feature_extraction_steps + postprocessing_steps)\n",
        "\n",
        "    # Read data\n",
        "    logger.info('Reading data')\n",
@@ -1697,6 +1708,7 @@
        "  --batch=$PREPROCESS_BATCH_ID \\\n",
        "  --container-image=$DATAPROC_RUNTIME_CONTAINER_IMAGE \\\n",
        "  --region=$REGION \\\n",
+        "  --version='1.0.21' \\\n",
        "  --subnet='default' \\\n",
        "  --properties spark.executor.instances=2,spark.driver.cores=4,spark.executor.cores=4,spark.app.name=spark_preprocessing_job \\\n",
        "  -- --input_path=$PREPARED_FILE_PATH --lemmas_path=$LEMMA_DICTIONARY_PATH --gcs_output_path=$PROCESS_DATA_PATH --bq_output_table_uri=$BQ_OUTPUT_TABLE_URI --bucket=$BUCKET_NAME --project=$PROJECT_ID"
@@ -1954,7 +1966,7 @@
        "        \"accuracy\": round(accuracy_score(y_test, y_pred, sample_weight=get_weights(y_test)), 5),\n",
        "        \"f1_score\": round(f1_score(y_test, y_pred, sample_weight=get_weights(y_test), average=\"weighted\"), 5),\n",
        "        \"log_loss\": round(log_loss(y_test, y_pred_proba, sample_weight=get_weights(y_test)), 5),\n",
-        "        \"roc_auc\": round(roc_auc_score(y_test, y_pred_proba, multi_class='ovr'), 5)\n",
+        "        \"roc_auc\": round(roc_auc_score(y_test, y_pred_proba[:,1], sample_weight=get_weights(y_test), average=\"weighted\"), 5)\n",
        "    }\n",
        "    return metrics\n",
        "\n",
@@ -2709,7 +2721,11 @@
        "\n",
        "versions = registry.list_versions()\n",
        "for version in versions:\n",
-        "    registry.delete_version(version=version.version_id)\n",
+        "    if \"default\" not in version.version_aliases:\n",
+        "        registry.delete_version(version=version.version_id)\n",
+        "    else:\n",
+        "        model = registry.get_model(version=\"default\")\n",
+        "        model.delete()\n",
        "\n",
        "naive_bayes_train_job.delete()\n",
        "\n",
@@ -0,0 +1,70 @@
+tag,notebook,doc
+"AutoML, Text data",official/automl/automl-text-classification.ipynb,vertex-ai/docs/text-data/classification/train-model
+"AutoML, Text data",official/automl/sdk_automl_text_entity_extraction_online.ipynb,
+"AutoML, Text data",official/automl/sdk_automl_text_sentiment_analysis_online.ipynb,
+"AutoML, Tabular data",official/automl/sdk_automl_tabular_forecasting_batch.ipynb,vertex-ai/docs/tabular-data/forecasting/tutorials-samples
+"AutoML, Tabular Data",official/automl/automl_tabular_on_vertex_pipelines.ipynb,vertex-ai/docs/tabular-data/tabular-workflows/e2e-automl
+"AutoML, Tabular Data",official/automl/sdk_automl_tabular_regression_batch_bq.ipynb,
+"AutoML, Tabular Data",official/automl/sdk_automl_tabular_regression_batch_bq.ipynb,
+"AutoML, Forecasting",official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb,vertex-ai/docs/tabular-data/forecasting-arima/overview
+"AutoML, Forecasting",official/automl/sdk_automl_tabular_forecasting_batch.ipynb,
+"AutoML, Image data",official/automl/sdk_automl_text_sentiment_analysis_online.ipynb,
+"AutoML, Video data",official/automl/sdk_automl_text_sentiment_analysis_online.ipynb,
+"AutoML, Video data",official/automl/sdk_automl_video_classification_batch.ipynb,
+"AutoML, Video data",official/automl/sdk_automl_video_object_tracking_batch.ipynb,
+"AutoML, Video data",official/sdk/SDK_AutoML_Video_Classification.ipynb,
+"BigQuery, Vertex AI Workbench",official/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb,
+"BigQuery ML, Vertex AI Model Registry, Batch prediction",official/model_registry/bqml_vertexai_model_registry.ipynb,
+"BigQuery ML, Vertex AI Model Registry, Online prediction",official/bigquery_ml/bqml-online-prediction.ipynb,
+"BigQuery ML",official/structured_data/rapid_prototyping_bqml_automl.ipynb,
+Custom Training,official/custom/sdk-custom-image-classification-batch.ipynb,
+Custom Training,official/custom/sdk-custom-image-classification-online.ipynb,
+Custom Training,official/custom/SDK_Custom_Container_Prediction.ipynb,
+"Custom Training, BiqQuery dataset",official/custom/custom-tabular-bq-managed-dataset.ipynb,
+"Custom Training, TensorBoard",official/custom/custom-tabular-bq-managed-dataset.ipynb,
+"Custom Training, TensorBoard",official/tensorboard/tensorboard_custom_training_with_custom_container.ipynb,
+"Custom Training, TensorBoard",official/tensorboard/tensorboard_custom_training_with_prebuilt_container.ipynb
+"Custom Training, Managed dataset",official/sdk/SDK_Custom_Training_Python_Package_Managed_Text_Dataset_Tensorflow_Serving_Container.ipynb,
+"Custom Training, Distributed",official/training/multi_node_ddp_gloo_vertex_training_with_custom_container.ipynb,
+"Custom Training, Distributed",official/training/multi_node_ddp_nccl_vertex_training_with_custom_container.ipynb
+Vertex AI Experiments,official/experiments/comparing_pipeline_runs.ipynb,
+Vertex AI Experiments,official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb,
+Vertex AI Experiments,official/experiments/comparing_local_trained_models.ipynb,
+"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb,vertex-ai/docs/explainable-ai/overview
+"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb,vertex-ai/docs/explainable-ai/overview
+"Vertex Explainable AI, Image data",official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb,vertex-ai/docs/explainable-ai/overview
+"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb,vertex-ai/docs/explainable-ai/overview
+"Vertex Explainable AI, Tabular data",official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb,vertex-ai/docs/explainable-ai/overview
+Vertex ML Metadata,official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb,
+"Vertex Explainable AI, Image data",official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb,vertex-ai/docs/explainable-ai/overview
+Vertex AI Feature Store,official/feature_store/sdk-feature-store.ipynb,
+Vertex AI Feature Store,official/feature_store/sdk-feature-store-pandas.ipynb,
+Vertex AI Matching Engine,official/matching_engine/sdk_matching_engine_for_indexing.ipynb,
+Vertex ML Metadata,official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb,
+Vertex ML Metadata,official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb,
+"Vertex ML Metadata, Vertex AI Pipelines",official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb,
+"Vertex AI Model Evaluation, AutoML",official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb,
+"Vertex AI Model Evaluation, AutoML",official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb,
+"Vertex AI Model Evaluation, AutoML",official/model_evaluation/automl_text_classification_model_evaluation.ipynb,
+"Vertex AI Model Evaluation, AutoML",official/model_evaluation/automl_video_classification_model_evaluation.ipynb,
+"Vertex AI Model Evaluation, Custom Training",official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb,
+Model Monitoring,official/model_monitoring/model_monitoring.ipynb,
+Vertex AI Pipelines,official/pipelines/pipelines_intro_kfp.ipynb,
+Vertex AI Pipelines,official/pipelines/control_flow_kfp.ipynb,
+Vertex AI Pipelines,official/pipelines/metrics_viz_run_compare_kfp.ipynb,
+Vertex AI Pipelines,official/pipelines/lightweight_functions_component_io_kfp.ipynb,
+"Vertex AI Pipelines Image data",official/pipelines/google_cloud_pipeline_components_automl_images.ipynb,
+"Vertex AI Pipelines, Tabular data",official/pipelines/automl_tabular_classification_beans.ipynb,
+"Vertex AI Pipelines, Tabular data",official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb,
+"Vertex AI Pipelines, Tabular data",official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb,
+"Vertex AI Pipelines, Text data",official/pipelines/google_cloud_pipeline_components_automl_text.ipynb,
+"Vertex AI Pipelines, Text data",official/pipelines/google_cloud_pipeline_components_bqml_text.ipynb,
+Vertex AI Pipelines,official/pipelines/custom_model_training_and_batch_prediction.ipynb,
+Vertex AI Pipelines,official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb,
+Vertex AI Pipelines,official/pipelines/google_cloud_pipeline_components_model_upload_predict_evaluate.ipynb,
+"Vertex AI Training, Reduction Server, PyTorch",official/reduction_server/pytorch_distributed_training_reduction_server.ipynb,
+"Tabular Workflows, Vertex AI TabNet",official/tabnet/tabnet_vertex_tutorial.ipynb,
+"Tabular Workflows, Vertex AI TabNet, Vertex Explainablee AI",official/tabnet/ai-explanations-tabnet-algorithm.ipynb,
+"Tabular Workflows, Vertex AI TabNet, Vertex AI Pipelines",official/tabular_workflows/tabnet_on_vertex_pipelines.ipynb,
+"Tabular Workflows, Vertex AI Wide and Deep",official/tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb,
+Vertex AI Vizier,official/vizier/gapic-vizier-multi-objective-optimization.ipynb,vertex-ai/docs/vizier/using-vizier
@@ -39,4 +39,7 @@
 /tabular_workflows/tabnet_on_vertex_pipelines.ipynb @sakagarwal
 /tabular_workflows/wide_and_deep_on_vertex_pipelines.ipynb @sakagarwal
 /model_evaluation/custom_tabular_classification_model_evaluation.ipynb @soheilazangeneh
-/sdk/SDK_FBProphet_Forecasting_Online.ipynb @brianchunkang
+/sdk/SDK_FBProphet_Forecasting_Online.ipynb @brianchunkang
+/automl/sdk_automl_forecasting_hierarchical_batch.ipynb @ivanmkc
+/prediction/custom_batch_prediction_feature_filter.ipynb @soheilazangeneh
+/feature_store/feature_store_streaming_ingestion_sdk.ipynb @soheilazangeneh
@@ -1,6 +1,6 @@
-
 [AutoML Tabular Training and Prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-tabular-classification.ipynb)

+```
 Learn how to train and make predictions on an AutoML model based on a tabular dataset.

 The steps performed include the following:
@@ -11,8 +11,12 @@ The steps performed include the following:
 - Make a prediction by sending data.
 - Undeploy the `Model` resource.

+```
+
+
 [Create, train, and deploy an AutoML text classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-text-classification.ipynb)

+```
 Learn how to use `AutoML` to train a text classification model.

 The steps performed include:
@@ -25,8 +29,12 @@ The steps performed include:
 * Make an online prediction
 * Make a batch prediction

+```
+
+
 [AutoML training video classification model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb)

+```
 Learn how to create an AutoML video classification model from a Python script, and then do a batch prediction using the Vertex AI SDK.

 The steps performed include:
@@ -36,13 +44,12 @@ The steps performed include:
 - View the model evaluation.
 - Make a batch prediction.

+```

-* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
-
-* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

 [AutoML training text entity extraction model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb)

+```
 Learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK.

 The steps performed include:
@@ -54,8 +61,12 @@ The steps performed include:
 - Make a prediction.
 - Undeploy the `Model`.

+```
+
+
 [AutoML tabular forecasting model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb)

+```
 Learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.

 The steps performed include:
@@ -65,40 +76,26 @@ The steps performed include:
 - Obtain the evaluation metrics for the `Model` resource.
 - Make a batch prediction.

-[AutoML training image object detection model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb)
+```

-Learn how to create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK.
-
-The steps performed include:
-
- Create a Vertex `Dataset` resource.
- Train the model.
- View the model evaluation.
- Make a batch prediction.
-
-
-* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
-
-* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

 [AutoML training video action recognition model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb)

+```
 Learn how to create an AutoML video action recognition model from a Python script, and then do a batch prediction using the Vertex AI SDK.

 The steps performed include:

- Create a Vertex `Dataset` resource.
+- Create a `Vertex AI Dataset` resource.
 - Train the model.
 - View the model evaluation.
 - Make a batch prediction.
+```


-* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
-
-* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.
-
 [AutoML Tabular Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)

+```
 Learn how to create two regression models using [Vertex Pipelines](https://cloud.

 The steps performed are:
@@ -106,36 +103,48 @@ The steps performed are:
 - Create a training pipeline that reduces the search space from the default to save time.
 - Create a training pipeline that reuses the architecture search results from the previous pipeline to save time.

-[AutoML training text sentiment analysis model for online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb)
+```

-Learn how to create an AutoML text sentiment analysis model and deploy for online prediction from a Python script using the Vertex SDK.
+
+[Training an AutoML text sentiment analysis model for online predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb)
+
+```
+Learn how to create an AutoML text sentiment analysis model and deploy it for online predictions from a Python script using the Vertex AI SDK.

 The steps performed include:

- Create a Vertex `Dataset` resource.
- Create a training job for the model.
- View the model evaluation.
- Deploy the `Model` resource to a serving `Endpoint` resource.
- Make a prediction.
- Undeploy the `Model`.
+- Create a `Vertex AI Dataset` resource.
+- Create a training job for the AutoML model on the dataset.
+- View the model evaluation metrics.
+- Deploy the `Vertex AI Model` resource to a serving `Vertex AI Endpoint`.
+- Make a prediction request to the deployed model.
+- Undeploy the model from endpoint.
+- Perform clean up process.
+
+```
+

 [Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)

-Learn how to create an BQML ARIMA_PLUS model using a training [Vertex AI Pipeline](https://cloud.
+```
+Learn how to create an BigQuery ML ARIMA_PLUS model using a training [Vertex AI Pipeline](https://cloud.

 The steps performed are:

- Train the BQML ARIMA_PLUS model.
- View BQML model evaluation.
- Make a batch prediction with the BQML model.
+- Train the BigQuery ML ARIMA_PLUS model.
+- View BigQuery ML model evaluation.
+- Make a batch prediction with the BigQuery ML model.
 - Create a Vertex AI `Dataset` resource.
 - Train the Vertex AI Forecasting model.
 - View the Model evaluation.
 - Make a batch prediction with the Model.

+```

-[AutoML training tabular regression model for online prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb)

+[AutoML training tabular regression model for online prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb)
+
+```
 Learn how to create an AutoML tabular regression model and deploy for online prediction from a Python script using the Vertex AI SDK.

 The steps performed include:
@@ -147,9 +156,13 @@ The steps performed include:
 - Make a prediction.
 - Undeploy the `Model`.

+```
+
+
 [AutoML training video object tracking model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb)

-Learn how to create an AutoML video object tracking model from a Python script, and then do a batch prediction using the Vertex SDK.
+```
+Learn how to create an AutoML video object tracking model from a Python script, and then do a batch prediction using the Vertex AI SDK.

 The steps performed include:

@@ -157,14 +170,12 @@ The steps performed include:
 - Train the model.
 - View the model evaluation.
 - Make a batch prediction.
+```


-* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
-
-* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.
-
 [AutoML training tabular regression model for batch prediction using BigQuery](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb)

+```
 Learn how to create an AutoML tabular regression model and deploy it for batch prediction using the Vertex AI SDK for Python.

 The steps performed include:
@@ -175,3 +186,5 @@ The steps performed include:
 - Deploy the `Model` resource to a serving `Endpoint` resource.
 - Make a prediction.
 - Undeploy the `Model`.
+
+```
@@ -29,7 +29,7 @@
        "id": "JAPoU8Sm5E6e"
      },
      "source": [
-        "# Vertex AI SDK for Python: AutoML Tabular Training and Prediction\n",
+        "# Vertex AI SDK for Python: AutoML Tabular training and prediction\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
@@ -63,7 +63,7 @@
        "\n",
        "This tutorial demonstrates how to use the Vertex AI Python client library to train and deploy a tabular classification model for online prediction.\n",
        "\n",
-        "**Note**: you may incur charges for training, prediction, storage, or usage of other GCP products in connection with testing this SDK."
+        "**Note**: you may incur charges for training, prediction, storage, or usage of other Google Cloud products in connection with testing this SDK."
      ]
    },
    {
@@ -76,6 +76,11 @@
        "\n",
        "In this tutorial, you learn how to train and make predictions on an AutoML model based on a tabular dataset. Alternatively, you can train and make predictions on models by using the `gcloud` command-line tool or by using the online Cloud Console.\n",
        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI\n",
+        "- AutoML Tabular\n",
+        "\n",
        "The steps performed include the following:\n",
        "\n",
        "- Create a Vertex AI model training job.\n",
@@ -122,7 +127,9 @@
        "id": "install_aip"
      },
      "source": [
-        "## Installation"
+        "## Installation\n",
+        "\n",
+        "Install the packages required for executing this notebook."
      ]
    },
    {
@@ -135,55 +142,20 @@
      "source": [
        "import os\n",
        "\n",
-        "# The Google Cloud Notebook product has specific requirements\n",
-        "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n",
+        "# The Vertex AI Workbench Notebook product has specific requirements\n",
+        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\") and not os.getenv(\"VIRTUAL_ENV\")\n",
+        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\n",
        "\n",
-        "# Google Cloud Notebook requires dependencies to be installed with '--user'\n",
+        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
        "USER_FLAG = \"\"\n",
-        "if IS_GOOGLE_CLOUD_NOTEBOOK:\n",
-        "    USER_FLAG = \"--user\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b03b7f4487ff"
-      },
-      "source": [
-        "Install the latest version of the Vertex AI client library.\n",
+        "if IS_WORKBENCH_NOTEBOOK:\n",
+        "    USER_FLAG = \"--user\"\n",
        "\n",
-        "Run the following command in your virtual environment to install the Vertex SDK for Python:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "d489d38261dd"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} --upgrade google-cloud-aiplatform"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "install_storage"
-      },
-      "source": [
-        "Install the Cloud Storage library:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "qssss-KSlugo"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} --upgrade google-cloud-storage"
+        "# Install the packagesimport os\n",
+        "! pip3 install {USER_FLAG} -q --upgrade google-cloud-aiplatform \\\n",
+        "                                        google-cloud-storage"
      ]
    },
    {
@@ -29,7 +29,7 @@
        "id": "mThXALJl9Yue"
      },
      "source": [
-        "# Tabular Workflow: AutoML Tabular Pipeline\n",
+        "# AutoML Tabular Workflow pipelines\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
@@ -72,7 +72,12 @@
      "source": [
        "### Objective\n",
        "\n",
-        "In this tutorial, you learn how to create two regression models using [Vertex Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) downloaded from [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction) (GCPC). These pipelines will be Vertex AI Tabular Workflow pipelines which are maintained by Google. These pipelines will showcase different ways to customize the Vertex Tabular training process.\n",
+        "In this tutorial, you learn how to create two regression models using [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines/introduction) downloaded from [Google Cloud Pipeline Components](https://cloud.google.com/vertex-ai/docs/pipelines/components-introduction) (GCPC). These pipelines will be Vertex AI Tabular Workflow pipelines which are maintained by Google. These pipelines will showcase different ways to customize the Vertex Tabular training process.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `AutoML Training`\n",
+        "- `Vertex AI Datasets`\n",
        "\n",
        "The steps performed are:\n",
        "\n",
@@ -640,9 +645,7 @@
        "prediction_type = \"classification\"\n",
        "optimization_objective = \"minimize-log-loss\"\n",
        "target_column = \"deposit\"\n",
-        "data_source_csv_filenames = (\n",
-        "    \"gs://cloud-samples-data/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv\"\n",
-        ")\n",
+        "data_source_csv_filenames = \"gs://cloud-samples-data/vertex-ai/tabular-workflows/datasets/bank-marketing/train.csv\"\n",
        "data_source_bigquery_table_path = None  # format: bq://bq_project.bq_dataset.bq_table\n",
        "\n",
        "timestamp_split_key = None  # timestamp column name when using timestamp split\n",
@@ -45,8 +45,8 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb\">\n",
-        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb\" target='_blank'>      \n",
+        "    <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>                    \n",
@@ -66,17 +66,6 @@
        "This tutorial demonstrates how to use the Vertex AI SDK to create image object detection models and do batch prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:salads,iod"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -87,6 +76,11 @@
        "\n",
        "In this tutorial, you create an AutoML image object detection model from a Python script, and then do a batch prediction using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `AutoML Training`\n",
+        "- `Vertex AI Datasets`\n",
+        "\n",
        "The steps performed include:\n",
        "\n",
        "- Create a Vertex `Dataset` resource.\n",
@@ -101,6 +95,17 @@
        "* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready."
      ]
    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:salads,iod"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the Salads category of the [OpenImages dataset](https://www.tensorflow.org/datasets/catalog/open_images_v4) from [TensorFlow Datasets](https://www.tensorflow.org/datasets/catalog/overview). This dataset does not require any feature engineering. The version of the dataset you will use in this tutorial is stored in a public Cloud Storage bucket. The trained model predicts the bounding box locations and the corresponding type of salad items in an image from a class of five items: salad, seafood, tomato, baked goods, or cheese."
+      ]
+    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -44,7 +44,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_tabular_regression_batch_bq.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -74,7 +74,7 @@
        "\n",
        "In this tutorial, you learn how to create an AutoML tabular regression model and deploy it for batch prediction using the Vertex AI SDK for Python. You can alternatively create and deploy models using the `gcloud` command-line tool or batch using the Cloud Console.\n",
        "\n",
-        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
        "\n",
        "- Vertex AI Datasets (Tabular)\n",
        "- Vertex AI Training (AutoML Tabular Training)\n",
@@ -33,13 +33,13 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb\">\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb\">\n",
-        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_regression_online_bq.ipynb\" target='_blank'>      \n",
+        "    <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
@@ -116,39 +116,6 @@
        "to generate a cost estimate based on your projected usage."
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "setup_local"
-      },
-      "source": [
-        "### Set up your local development environment\n",
-        "\n",
-        "If you are using Colab or Google Cloud Notebooks, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
-        "\n",
-        "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
-        "\n",
-        "- The Cloud Storage SDK\n",
-        "- Git\n",
-        "- Python 3\n",
-        "- virtualenv\n",
-        "- Jupyter notebook running in a virtual environment with Python 3\n",
-        "\n",
-        "The Cloud Storage guide to [Setting up a Python development environment](https://cloud.google.com/python/setup) and the [Jupyter installation guide](https://jupyter.org/install) provide detailed instructions for meeting these requirements. The following steps provide a condensed set of instructions:\n",
-        "\n",
-        "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
-        "\n",
-        "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
-        "\n",
-        "3. [Install virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv) and create a virtual environment that uses Python 3.  Activate the virtual environment.\n",
-        "\n",
-        "4. To install Jupyter, run `pip3 install jupyter` on the command-line in a terminal shell.\n",
-        "\n",
-        "5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
-        "\n",
-        "6. Open this notebook in the Jupyter Notebook Dashboard.\n"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -168,35 +135,8 @@
      },
      "outputs": [],
      "source": [
-        "import os\n",
-        "\n",
-        "# Google Cloud Notebook\n",
-        "if os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
-        "    USER_FLAG = \"--user\"\n",
-        "else:\n",
-        "    USER_FLAG = \"\"\n",
-        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "install_storage"
-      },
-      "source": [
-        "Install the latest GA version of *google-cloud-storage* library as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_storage"
-      },
-      "outputs": [],
-      "source": [
-        "! pip3 install -U google-cloud-storage $USER_FLAG"
+        "! pip3 install --upgrade google-cloud-aiplatform \\\n",
+        "                            google-cloud-storage"
      ]
    },
    {
@@ -205,9 +145,7 @@
        "id": "restart"
      },
      "source": [
-        "### Restart the kernel\n",
-        "\n",
-        "Once you've installed the additional packages, you need to restart the notebook kernel so it can find the packages."
+        "### Colab only: Uncomment the following cell to restart the kernel"
      ]
    },
    {
@@ -218,14 +156,11 @@
      },
      "outputs": [],
      "source": [
-        "import os\n",
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
        "\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    # Automatically restart kernel after installs\n",
-        "    import IPython\n",
-        "\n",
-        "    app = IPython.Application.instance()\n",
-        "    app.kernel.do_shutdown(True)"
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
      ]
    },
    {
@@ -242,20 +177,11 @@
        "\n",
        "### Set up your Google Cloud project\n",
        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
+        "**If you dont know your project ID,** try the following\n",
        "\n",
-        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
-        "\n",
-        "3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
-        "\n",
-        "4. If you are running this notebook locally, you will need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
-        "\n",
-        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\n",
-        "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
+        "- Run `gcloud config list`\n",
+        "- Run `gcloud projects list`\n",
+        "- See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)\n"
      ]
    },
    {
@@ -266,33 +192,10 @@
      },
      "outputs": [],
      "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_project_id"
-      },
-      "outputs": [],
-      "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"[your-project-id]\":\n",
-        "    # Get your GCP project id from gcloud\n",
-        "    shell_output = ! gcloud config list --format 'value(core.project)' 2>/dev/null\n",
-        "    PROJECT_ID = shell_output[0]\n",
-        "    print(\"Project ID:\", PROJECT_ID)"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_gcloud_project_id"
-      },
-      "outputs": [],
-      "source": [
-        "! gcloud config set project $PROJECT_ID"
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the project ID\n",
+        "! gcloud config set project {PROJECT_ID}"
      ]
    },
    {
@@ -303,16 +206,7 @@
      "source": [
        "#### Region\n",
        "\n",
-        "You can also change the `REGION` variable, which is used for operations\n",
-        "throughout the rest of this notebook.  Below are regions supported for Vertex AI. We recommend that you choose the region closest to you.\n",
-        "\n",
-        "- Americas: `us-central1`\n",
-        "- Europe: `europe-west4`\n",
-        "- Asia Pacific: `asia-east1`\n",
-        "\n",
-        "You may not use a multi-regional bucket for training with Vertex AI. Not all regions provide support for all Vertex AI services.\n",
-        "\n",
-        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI Regions](https://cloud.google.com/vertex-ai/docs/general/locations)"
      ]
    },
    {
@@ -326,30 +220,6 @@
        "REGION = \"us-central1\"  # @param {type: \"string\"}"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "timestamp"
-      },
-      "source": [
-        "#### Timestamp\n",
-        "\n",
-        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "timestamp"
-      },
-      "outputs": [],
-      "source": [
-        "from datetime import datetime\n",
-        "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -358,53 +228,64 @@
      "source": [
        "### Authenticate your Google Cloud account\n",
        "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below.\n",
        "\n",
-        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
+        "**1. Vertex AI workbench** \n",
+        "- Do nothing as you are already authenticated.\n",
        "\n",
-        "**Otherwise**, follow these steps:\n",
-        "\n",
-        "In the Cloud Console, go to the [Create service account key](https://console.cloud.google.com/apis/credentials/serviceaccountkey) page.\n",
-        "\n",
-        "**Click Create service account**.\n",
-        "\n",
-        "In the **Service account name** field, enter a name, and click **Create**.\n",
-        "\n",
-        "In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex\" into the filter box, and select **Vertex Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
-        "\n",
-        "Click Create. A JSON file that contains your key downloads to your local environment.\n",
-        "\n",
-        "Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
+        "**2. Local JupyterLab Instance, uncomment and run:**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "gcp_authenticate"
+        "id": "457c78b08293"
      },
      "outputs": [],
      "source": [
-        "# If you are running this notebook in Colab, run this cell and follow the\n",
-        "# instructions to authenticate your GCP account. This provides access to your\n",
-        "# Cloud Storage bucket and lets you submit training jobs and prediction\n",
-        "# requests.\n",
-        "\n",
-        "import os\n",
-        "import sys\n",
-        "\n",
-        "# If on Google Cloud Notebook, then don't execute this code\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\"):\n",
-        "    if \"google.colab\" in sys.modules:\n",
-        "        from google.colab import auth as google_auth\n",
-        "\n",
-        "        google_auth.authenticate_user()\n",
-        "\n",
-        "    # If you are running this notebook locally, replace the string below with the\n",
-        "    # path to your service account key and run this cell to authenticate your GCP\n",
-        "    # account.\n",
-        "    elif not os.getenv(\"IS_TESTING\"):\n",
-        "        %env GOOGLE_APPLICATION_CREDENTIALS ''"
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d3e571ce6c56"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "984a0526fb68"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "764c0ac706e1"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "- See all the authentication options here: [Google Cloud Platform Jupyter Notebook Authentication Guide](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_authentication_guide.ipynb)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "276cfd2c6167"
+      },
+      "source": [
+        "### Create a Cloud Storage bucket\n",
+        "Create a storage bucket to store intermediate artifacts such as datasets"
      ]
    },
    {
@@ -415,20 +296,7 @@
      },
      "outputs": [],
      "source": [
-        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "autoset_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "if BUCKET_URI == \"\" or BUCKET_URI is None or BUCKET_URI == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "BUCKET_URI = \"gs://test-bucket-unique\"  # @param {type:\"string\"}"
      ]
    },
    {
@@ -451,35 +319,12 @@
        "! gsutil mb -l $REGION $BUCKET_URI"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "validate_bucket"
-      },
-      "source": [
-        "Finally, validate access to your Cloud Storage bucket by examining its contents:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "validate_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil ls -al $BUCKET_URI"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "setup_vars"
      },
      "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
        "### Import libraries and define constants"
      ]
    },
@@ -491,7 +336,11 @@
      },
      "outputs": [],
      "source": [
-        "import google.cloud.aiplatform as aiplatform"
+        "import os\n",
+        "\n",
+        "import google.cloud.aiplatform as aiplatform\n",
+        "\n",
+        "display_name = \"gsod_unique\""
      ]
    },
    {
@@ -574,7 +423,7 @@
      "outputs": [],
      "source": [
        "dataset = aiplatform.TabularDataset.create(\n",
-        "    display_name=\"NOAA historical weather data\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"NOAA historical weather data_unique\",\n",
        "    bq_source=[IMPORT_FILE],\n",
        ")\n",
        "\n",
@@ -645,7 +494,7 @@
      "outputs": [],
      "source": [
        "job = aiplatform.AutoMLTabularTrainingJob(\n",
-        "    display_name=\"gsod_\" + TIMESTAMP,\n",
+        "    display_name=display_name,\n",
        "    optimization_prediction_type=\"regression\",\n",
        "    optimization_objective=\"minimize-rmse\",\n",
        "    column_transformations=TRANSFORMATIONS,\n",
@@ -688,7 +537,7 @@
      "source": [
        "model = job.run(\n",
        "    dataset=dataset,\n",
-        "    model_display_name=\"gsod_\" + TIMESTAMP,\n",
+        "    model_display_name=display_name,\n",
        "    training_fraction_split=0.6,\n",
        "    validation_fraction_split=0.2,\n",
        "    test_fraction_split=0.2,\n",
@@ -705,33 +554,22 @@
      },
      "source": [
        "## Review model evaluation scores\n",
-        "After your model has finished training, you can review the evaluation scores for it.\n",
-        "\n",
-        "First, you need to get a reference to the new model. As with datasets, you can either use the reference to the model variable you created when you deployed the model or you can list all of the models in your project."
+        "After your model training has finished, you can review the evaluation scores for it using the list_model_evaluations() method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
-        "id": "evaluate_the_model:mbsdk"
+        "id": "4f674dcea72c"
      },
      "outputs": [],
      "source": [
-        "# Get model resource ID\n",
-        "models = aiplatform.Model.list(filter=\"display_name=gsod_\" + TIMESTAMP)\n",
-        "\n",
-        "# Get a reference to the Model Service client\n",
-        "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
-        "model_service_client = aiplatform.gapic.ModelServiceClient(\n",
-        "    client_options=client_options\n",
-        ")\n",
-        "\n",
-        "model_evaluations = model_service_client.list_model_evaluations(\n",
-        "    parent=models[0].resource_name\n",
-        ")\n",
-        "model_evaluation = list(model_evaluations)[0]\n",
-        "print(model_evaluation)"
+        "model_evaluations = model.list_model_evaluations()\n",
+        "if len(model_evaluations) > 0:\n",
+        "    eval_res = model_evaluations[0].to_dict()\n",
+        "    evaluation_metrics = eval_res[\"metrics\"]\n",
+        "print(evaluation_metrics)"
      ]
    },
    {
@@ -881,23 +719,22 @@
      },
      "outputs": [],
      "source": [
-        "delete_all = True\n",
+        "# Delete the dataset using the Vertex dataset object\n",
+        "dataset.delete()\n",
        "\n",
-        "if delete_all:\n",
-        "    # Delete the dataset using the Vertex dataset object\n",
-        "    dataset.delete()\n",
+        "# Delete the model using the Vertex model object\n",
+        "model.delete()\n",
        "\n",
-        "    # Delete the model using the Vertex model object\n",
-        "    model.delete()\n",
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.delete()\n",
        "\n",
-        "    # Delete the endpoint using the Vertex endpoint object\n",
-        "    endpoint.delete()\n",
+        "# Delete the AutoML trainig job\n",
+        "job.delete()\n",
        "\n",
-        "    # Delete the AutoML trainig job\n",
-        "    job.delete()\n",
+        "delete_bucket = False\n",
        "\n",
-        "    if os.getenv(\"IS_TESTING\"):\n",
-        "        ! gsutil rm -r $BUCKET_URI"
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil rm -r $BUCKET_URI"
      ]
    }
  ],
@@ -44,8 +44,8 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/master/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb\">\n",
-        "     <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_text_entity_extraction_online.ipynb\" target='_blank'>     \n",
+        "    <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
        "  </td>\n",
@@ -73,7 +73,12 @@
      "source": [
        "### Objective\n",
        "\n",
-        "In this tutorial, you learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
+        "In this tutorial, you learn how to create an AutoML text entity extraction model and deploy for online prediction from a Python script using the Vertex AI SDK. You can alternatively create and deploy models using the `gcloud` command-line tool or online using the Cloud Console.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `AutoML Training`\n",
+        "- `Vertex AI Datasets`\n",
        "\n",
        "The steps performed include:\n",
        "\n",
@@ -44,7 +44,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_text_sentiment_analysis_online.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_action_recognition_batch.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -44,8 +44,8 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "          <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb\">\n",
-        "            <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"> \n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_classification_batch.ipynb\" target='_blank'>            \n",
+        "    <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"> \n",
        "Open in Vertex AI Workbench \n",
        "    </a>\n",
        "  </td>\n",
@@ -44,7 +44,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/automl/sdk_automl_video_object_tracking_batch.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -1,6 +1,6 @@
-
 [Online prediction with BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb)

+```
 Learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.

 The steps performed include:
@@ -12,3 +12,5 @@ The steps performed include:
 - Deploying the model to an endpoint on Vertex AI
 - Making sample online predictions to the model endpoint

+```
+
@@ -1,6 +1,25 @@
+[Training and deploying a sales forecasting model using FBProphet and Vertex AI](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/SDK_FBProphet_Forecasting_Online.ipynb)
+
+```
+The objective of this notebook is to create, deploy and serve a custom forecasting model on Vertex AI.
+
+The steps performed include:
+- Train a model locally that forecasts sales for the given number of days.
+- Train another model that uses both sales and weather data for sales prediction.
+- Save both the models.
+- Build a FastAPI server to handle the predictions for the chosen model.
+- Build a custom container image of the serving application with the model artifacts.
+- Upload the model to Vertex AI Model Registry.
+- Deploy the model to a Vertex AI Endpoint.
+- Send online prediction requests to the deployed model.
+- Clean up the resources created in this session.
+
+```
+

 [Custom training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)

+```
 Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.

 The steps performed include:
@@ -9,8 +28,12 @@ The steps performed include:
 - Upload the trained model artifacts as a `Model` resource.
 - Make a batch prediction.

+```
+
+
 [Profile model training performance using Profiler](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom_training_tensorboard_profiler.ipynb)

+```
 Learn how to enable Vertex AI TensorBoard Profiler for custom training jobs.

 The steps performed include:
@@ -20,9 +43,12 @@ The steps performed include:
 - Create and run a custom training job
 - View the TensorBoard Profiler dashboard

+```
+

 [Training a TensorFlow model on BigQuery data](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb)

+```
 Learn how to create a custom-trained model from a Python script in a Docker container using the Vertex AI SDK for Python, and then get a prediction from the deployed model by sending data.

 The steps performed include:
@@ -33,8 +59,12 @@ The steps performed include:
 - Make a prediction.
 - Undeploy the `Model` resource.

+```
+
+
 [Custom training and online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.ipynb)

+```
 Learn to use `Vertex AI Training` to create a custom-trained model from a Python script in a Docker container, and learn to use `Vertex AI Prediction` to do a prediction on the deployed model by sending data.

 The steps performed include:
@@ -46,8 +76,12 @@ The steps performed include:
 - Make a prediction.
 - Undeploy the `Model` resource.

+```
+
+
 [Deploying Iris-detection model using FastAPI and Vertex AI custom container serving](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/SDK_Custom_Container_Prediction.ipynb)

+```
 Learn how to create, deploy and serve a custom classification model on Vertex AI.

 The steps performed include:
@@ -57,3 +91,6 @@ The steps performed include:
 - Build a FastAPI server to handle predictions and health checks.
 - Build a custom container with model artifacts.
 - Upload and deploy custom container to Vertex AI Endpoints.
+
+```
+
@@ -44,7 +44,7 @@
        "    </a>\n",
        "   </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/SDK_FBProphet_Forecasting_Online.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/custom/SDK_FBProphet_Forecasting_Online.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -413,18 +413,6 @@
        "- Split train and test data"
      ]
    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "b5dfe6890137"
-      },
-      "outputs": [],
-      "source": [
-        "# Define the BigQuery source dataset\n",
-        "BQ_SOURCE = \"bigquery-public-data.ml_datasets.penguins\""
-      ]
-    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -451,10 +439,6 @@
        "# Drop unusable rows\n",
        "df = df.replace(to_replace=NA_VALUES, value=np.NaN).dropna()\n",
        "\n",
-        "df_numeric = df.select_dtypes(include=\"number\").astype(\"float32\")\n",
-        "df_numeric = (df_numeric - df_numeric.mean()) / df_numeric.std()\n",
-        "df[df_numeric.columns] = df_numeric\n",
-        "\n",
        "# Convert categorical columns to numeric\n",
        "df[\"island\"], _ = pd.factorize(df[\"island\"])\n",
        "df[\"species\"], _ = pd.factorize(df[\"species\"])\n",
@@ -465,45 +449,6 @@
        "df_holdout = df[~df.index.isin(df_train.index)]"
      ]
    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4e6a4fd28bab"
-      },
-      "source": [
-        "### Write the training dataset to BigQuery\n",
-        "Use the BigQuery SDK to create a dataset and write your training dataframe to it."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8b41bbd2380a"
-      },
-      "outputs": [],
-      "source": [
-        "# Write training dataset to BigQuery\n",
-        "\n",
-        "# Create BigQuery dataset\n",
-        "dataset_id = \"dataset_id_unique\"\n",
-        "bq_dataset = bigquery.Dataset(f\"{PROJECT_ID}.{dataset_id}\")\n",
-        "bq_dataset = bq_client.create_dataset(bq_dataset, exists_ok=True)\n",
-        "\n",
-        "# Reference: https://cloud.google.com/bigquery/docs/samples/bigquery-load-table-dataframe\n",
-        "table_id = \"table_id_unique\"\n",
-        "job = bq_client.load_table_from_dataframe(\n",
-        "    dataframe=df_train,\n",
-        "    destination=f\"{PROJECT_ID}.{dataset_id}.{table_id}\",\n",
-        ")\n",
-        "\n",
-        "job.result()\n",
-        "\n",
-        "BQ_TRAIN_URI = str(job.destination)\n",
-        "\n",
-        "BQ_TRAIN_URI"
-      ]
-    },
    {
      "cell_type": "markdown",
      "metadata": {
@@ -517,6 +462,20 @@
        "See more info here: https://cloud.google.com/vertex-ai/docs/training/using-managed-datasets"
      ]
    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "7fa452ee5c75"
+      },
+      "outputs": [],
+      "source": [
+        "# Create BigQuery dataset\n",
+        "bq_dataset_id = f\"{PROJECT_ID}.dataset_id_unique\"\n",
+        "bq_dataset = bigquery.Dataset(bq_dataset_id)\n",
+        "bq_client.create_dataset(bq_dataset, exists_ok=True)"
+      ]
+    },
    {
      "cell_type": "code",
      "execution_count": null,
@@ -525,8 +484,10 @@
      },
      "outputs": [],
      "source": [
-        "dataset = aiplatform.TabularDataset.create(\n",
-        "    display_name=\"sample-penguins\", bq_source=f\"bq://{BQ_TRAIN_URI}\"\n",
+        "dataset = aiplatform.TabularDataset.create_from_dataframe(\n",
+        "    df_source=df_train,\n",
+        "    staging_path=f\"bq://{bq_dataset_id}.table-unique\",\n",
+        "    display_name=\"sample-penguins\",\n",
        ")"
      ]
    },
@@ -542,7 +503,9 @@
        "\n",
        "- **Use a Vertex AI pre-built container**. If you use a pre-built training container, you must additionally specify a Python package to install into the container image. This Python package contains your training code.\n",
        "\n",
-        "- **Use your own custom container image**. If you use your own container, the container image must contain your training code."
+        "- **Use your own custom container image**. If you use your own container, the container image must contain your training code.\n",
+        "\n",
+        "You will use a pre-built container for this demo."
      ]
    },
    {
@@ -591,7 +554,6 @@
        "In the next cell, write the contents of the training script, `task.py`. In summary, the script does the following:\n",
        "\n",
        "- Loads the data from the BigQuery table using the BigQuery Python client library.\n",
-        "- Loads the pre-calculated mean and standard deviation from the Cloud Storage bucket.\n",
        "- Builds a model using TF.Keras model API.\n",
        "- Compiles the model (`compile()`).\n",
        "- Sets a training distribution strategy according to the argument `args.distribute`.\n",
@@ -631,15 +593,9 @@
        "\n",
        "# Read args\n",
        "parser = argparse.ArgumentParser()\n",
-        "parser.add_argument('--label_column', dest='label_column',\n",
-        "                    required=True, type=str,\n",
-        "                    help='Label column.')\n",
-        "parser.add_argument('--epochs', dest='epochs',\n",
-        "                    default=10, type=int,\n",
-        "                    help='Number of epochs.')\n",
-        "parser.add_argument('--batch_size', dest='batch_size',\n",
-        "                    default=10, type=int,\n",
-        "                    help='Batch size.')\n",
+        "parser.add_argument('--label_column', required=True, type=str)\n",
+        "parser.add_argument('--epochs', default=10, type=int)\n",
+        "parser.add_argument('--batch_size', default=10, type=int)\n",
        "args = parser.parse_args()\n",
        "\n",
        "# Set up training variables\n",
@@ -904,8 +860,7 @@
        "\n",
        "You can then run a quick evaluation on the prediction results:\n",
        "1. `np.argmax`: Convert each list of confidence levels to a label\n",
-        "2. Compare the predicted labels to the actual labels\n",
-        "3. Calculate `accuracy` as `correct/total`"
+        "2. Print predictions"
      ]
    },
    {
@@ -919,11 +874,7 @@
        "predictions = endpoint.predict(instances=holdout_x)\n",
        "y_predicted = np.argmax(predictions.predictions, axis=1)\n",
        "\n",
-        "correct = sum(y_predicted == np.array(holdout_y))\n",
-        "accuracy = len(y_predicted)\n",
-        "print(\n",
-        "    f\"Correct predictions = {correct}, Total predictions = {accuracy}, Accuracy = {correct/accuracy}\"\n",
-        ")"
+        "y_predicted"
      ]
    },
    {
@@ -1,12 +1,21 @@
-
 [Compare pipeline runs with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_pipeline_runs.ipynb)

+```
 Learn how to use `Vertex AI Experiments` to log a pipeline job and compare different pipeline jobs.

+The steps performed include:
+
+* Formalize a training component
+* Build a training pipeline
+* Run several Pipeline jobs and log their results
+* Compare different Pipeline jobs
+
+```


 [Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_local_trained_models.ipynb)

+```
 Learn how to use Vertex AI Experiments to compare and evaluate model experiments.

 The steps performed include:
@@ -15,9 +24,15 @@ The steps performed include:
 - log the loss and metrics on every epoch to TensorBoard
 - log the evaluation metrics

+```
+

 [Build Vertex AI Experiment lineage for custom training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb)

+```
 Learn how to integrate preprocessing code in a Vertex AI experiments.


+
+```
+
@@ -72,7 +72,22 @@
      "source": [
        "### Objective\n",
        "\n",
-        "In this notebook, you learn how to integrate preprocessing code in a Vertex AI experiments. Also you build the experiment lineage lets you record, analyze, debug, and audit metadata and artifacts produced along your ML journey."
+        "In this notebook, you learn how to integrate preprocessing code in a Vertex AI experiments. Also you build the experiment lineage lets you record, analyze, debug, and audit metadata and artifacts produced along your ML journey.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex ML Metadata\n",
+        "- Vertex AI Experiments\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Execute module for preprocessing data\n",
+        "  - Create a dataset artifact\n",
+        "  - Log parameters\n",
+        "-  Execute module for training the model\n",
+        "  - Log parameters\n",
+        "  - Create model artifact\n",
+        "  - Assign tracking lineage to dataset, model and parameters"
      ]
    },
    {
@@ -61,7 +61,7 @@
      "source": [
        "## Overview\n",
        "\n",
-        "Depending on the model life cycle of your data science team, you would like to experiment and track training Pipeline runs and its associated parameters. Then, you would to compare runs of these Pipelines to each others in order to figure out which is the best configuration generates the model you will register in the Vertex AI Model Registry."
+        "Depending on the model life cycle of your data science team, you would like to experiment and track training pipeline runs and its associated parameters. Then, you would to compare runs of these Pipelines to each others in order to figure out which is the best configuration generates the model you will register in the Vertex AI Model Registry."
      ]
    },
    {
@@ -74,7 +74,12 @@
        "\n",
        "In this notebook, you learn how to use `Vertex AI Experiments` to log a pipeline job and compare different pipeline jobs.\n",
        "\n",
-        "The steps covered include:\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI Pipelines\n",
+        "- Vertex AI Experiments\n",
+        "\n",
+        "The steps performed include:\n",
        "\n",
        "* Formalize a training component\n",
        "* Build a training pipeline\n",
@@ -1,6 +1,6 @@
-
 [Custom training tabular regression model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)

+```
 Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.

 The steps performed include:
@@ -11,8 +11,12 @@ The steps performed include:
 - Upload the trained model artifacts and explanations as a `Model` resource.
 - Make a batch prediction with explanations.

+```
+
+
 [AutoML training tabular binary classification model for batch explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb)

+```
 Learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Batch Prediction` to make predictions with explanations.

 The steps performed include:
@@ -22,13 +26,12 @@ The steps performed include:
 - View the model evaluation metrics for the trained model.
 - Make a batch prediction request with explainability.

+```

-* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
-
-* Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

 [AutoML training tabular classification model for online explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb)

+```
 Learn how to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations.

 The steps performed include:
@@ -41,8 +44,12 @@ The steps performed include:
 - Make an online prediction request with explainability.
 - Undeploy the `Model` resource.

+```
+
+
 [Custom training image classification model for online prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)

+```
 Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Prediction` to make an online prediction request with explanations.

 The steps performed include:
@@ -56,8 +63,31 @@ The steps performed include:
 - Make a prediction with explanation.
 - Undeploy the `Model` resource.

+```
+
+
+[Custom training tabular regression model for online prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb)
+
+```
+Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Prediction` to make an online prediction request with explanations.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- View the model evaluation for the trained model.
+- Set explanation parameters for when the model is deployed.
+- Upload the trained model artifacts and explanations as a `Model` resource.
+- Create a serving `Endpoint` resource.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction with explanation.
+- Undeploy the `Model` resource.
+
+```
+
+
 [Custom training tabular regression model for online prediction with explainabilty using get_metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain_get_metadata.ipynb)

+```
 Learn how to create a custom model from a Python script in a Google prebuilt Docker container using the Vertex SDK, and then do a prediction with explanations on the deployed model by sending data.

 The steps performed include:
@@ -72,8 +102,12 @@ The steps performed include:
 - Make a prediction with explanation.
 - Undeploy the `Model` resource.

+```
+
+
 [Custom training image classification model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)

+```
 Learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.

 The steps performed include:
@@ -83,3 +117,6 @@ The steps performed include:
 - Set explanation parameters for when the model is deployed.
 - Upload the trained model artifacts and explanation parameters as a `Model` resource.
 - Make a batch prediction with explanations.
+
+```
+
@@ -62,7 +62,7 @@
        "## Overview\n",
        "\n",
        "\n",
-        "This tutorial demonstrates how to use the Vertex SDK to train and deploy a custom tabular regression model for online prediction with explanation."
+        "This tutorial demonstrates how to use the Vertex AI SDK to train and deploy a custom tabular regression model for online prediction with explanation."
      ]
    },
    {
@@ -73,7 +73,16 @@
      "source": [
        "### Objective\n",
        "\n",
-        "In this tutorial, you learn how to create a custom model from a Python script in a Google prebuilt Docker container using the Vertex SDK, and then do a prediction with explanations on the deployed model by sending data. You can alternatively create custom models using `gcloud` command-line tool or online using Cloud Console.\n",
+        "In this tutorial, you learn how to create a custom model from a Python script in a Google prebuilt Docker container using the Vertex AI SDK, and then do a prediction with explanations on the deployed model by sending data. You can alternatively create custom models using `gcloud` command-line tool or online using Cloud Console.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Training`\n",
+        "- `Vertex AI Online Prediction`\n",
+        "- `Vertex Explainable AI`\n",
+        "- `Vertex AI Model` resource\n",
+        "- `Vertex AI Endpoint` resource\n",
+        "\n",
        "\n",
        "The steps performed include:\n",
        "\n",
@@ -1,20 +1,24 @@
+[Using Vertex AI Feature Store with Pandas Dataframe](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb)

-[Using Vertex AI Feature Store with pandas DataFrame](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store-pandas.ipynb)
-
-Learn how to use `Vertex AI Feature Store` with pandas DataFrame.
+```
+Learn how to use `Vertex AI Feature Store` with pandas Dataframe.

 The steps performed include:

 - Ingest Feature values from Pandas DataFrame into Feature Store's Entity types.
- Read Entity Feature values from Online Feature Store into Pandas DataFrame.
- Batch serve Feature values from your Feature Store into Pandas DataFrame.
+- Read Entity feature values from Online Feature Store into Pandas DataFrame.
+- Batch serve feature values from your Feature Store into Pandas DataFrame.


 - Online serving with updated feature values.
 - Point-in-time correctness to fetch feature values for training.

+```
+
+
 [Online and Batch predictions using Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store.ipynb)

+```
 Learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.

 The steps performed include:
@@ -23,3 +27,6 @@ The steps performed include:
 - Import feature data into `Vertex AI Feature Store` resource.
 - Serve online prediction requests using the imported features.
 - Access imported features in offline jobs, such as training jobs.
+
+```
+
@@ -0,0 +1,774 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "ur8xi4C7S06n"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2022 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "JAPoU8Sm5E6e"
+      },
+      "source": [
+        "# Feature Store: Streaming ingestion SDK\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
+        "      View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td>\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/feature_store/feature_store_streaming_ingestion_sdk.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      Open in Vertex AI Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "24743cf4a1e1"
+      },
+      "source": [
+        "**_NOTE_**: This notebook has been tested in the following environment:\n",
+        "\n",
+        "* Python version = 3.9"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates how to use Vertex AI Feature Store's streaming ingestion at the SDK layer."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d975e698c9a4"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to ingest features from a `Pandas DataFrame` into your Vertex AI Feature Store using `write_feature_values` method from the Vertex AI SDK.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI Feature Store\n",
+        "\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Create `Feature Store`\n",
+        "- Create new `Entity Type` for your `Feature Store`\n",
+        "- Ingest feature values from `Pandas DataFrame` into `Feature Store`'s `Entity Types`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "08d289fa873f"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this notebook is the penguins dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). This dataset has the following features: `culmen_length_mm`, `culmen_depth_mm`, `flipper_length_mm`, `body_mass_g`, `species`, and `sex`."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aed92deeb4a0"
+      },
+      "source": [
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "\n",
+        "Learn about [Vertex AI\n",
+        "pricing](https://cloud.google.com/vertex-ai/pricing) and use the [Pricing\n",
+        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i7EUnXsZhAGF"
+      },
+      "source": [
+        "## Installation\n",
+        "\n",
+        "Install the following packages required to execute this notebook."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2b4ef9b72d43"
+      },
+      "outputs": [],
+      "source": [
+        "# Install the packages\n",
+        "! pip3 install --upgrade google-cloud-aiplatform\\\n",
+        "                         google-cloud-bigquery\\\n",
+        "                         numpy\\\n",
+        "                         pandas\\\n",
+        "                         pyarrow -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "58707a750154"
+      },
+      "source": [
+        "### Colab only: Uncomment the following cell to restart the kernel."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f200f10a1da3"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs so that your environment can access the new packages\n",
+        "# import IPython\n",
+        "\n",
+        "# app = IPython.Application.instance()\n",
+        "# app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BF1j6f9HApxa"
+      },
+      "source": [
+        "## Before you begin\n",
+        "\n",
+        "### Set up your Google Cloud project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. [Select or create a Google Cloud project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "\n",
+        "3. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
+        "\n",
+        "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk)."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WReHDGG5g0XY"
+      },
+      "source": [
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, try the following:\n",
+        "* Run `gcloud config list`.\n",
+        "* Run `gcloud projects list`.\n",
+        "* See the support page: [Locate the project ID](https://support.google.com/googleapi/answer/7014113)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oM1iC_MfAts1"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Set the project id\n",
+        "! gcloud config set project {PROJECT_ID}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "source": [
+        "#### Region\n",
+        "\n",
+        "You can also change the `REGION` variable used by Vertex AI. Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "kljmKgilI_de"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBCra4QMA2wR"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "74ccc9e52986"
+      },
+      "source": [
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "de775a3773ba"
+      },
+      "source": [
+        "**2. Local JupyterLab instance, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "254614fa0c46"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ef21552ccea8"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "603adbbf0532"
+      },
+      "outputs": [],
+      "source": [
+        "# from google.colab import auth\n",
+        "# auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f6b2ccc891ed"
+      },
+      "source": [
+        "**4. Service account or other**\n",
+        "* See how to grant Cloud Storage permissions to your service account at https://cloud.google.com/storage/docs/gsutil/commands/iam#ch-examples."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EsCYkJ4IU-z4"
+      },
+      "source": [
+        "### UUID\n",
+        "\n",
+        "If you are in a live tutorial session, you might be using a shared test account or project. To avoid name collisions between users on resources created, you create a uuid for each instance session, and append it onto the name of resources you create in this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4jWj2DSTU9my"
+      },
+      "outputs": [],
+      "source": [
+        "import random\n",
+        "import string\n",
+        "\n",
+        "\n",
+        "# Generate a uuid of a specifed length(default=8)\n",
+        "def generate_uuid(length: int = 8) -> str:\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=length))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "960505627ddf"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PyQmSRbKA8r-"
+      },
+      "outputs": [],
+      "source": [
+        "import numpy as np\n",
+        "import pandas as pd\n",
+        "from google.cloud import aiplatform, bigquery"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "source": [
+        "### Initialize Vertex AI SDK for Python\n",
+        "\n",
+        "Initialize the Vertex AI SDK for Python for your project."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "0ep8KuQhI_df"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(project=PROJECT_ID, location=REGION)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "k5XsEiAuEWUJ"
+      },
+      "source": [
+        "## Download and prepare the data"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rOd7Ixa1pqBY"
+      },
+      "outputs": [],
+      "source": [
+        "def download_bq_table(bq_table_uri: str) -> pd.DataFrame:\n",
+        "    # Remove bq:// prefix if present\n",
+        "    prefix = \"bq://\"\n",
+        "    if bq_table_uri.startswith(prefix):\n",
+        "        bq_table_uri = bq_table_uri[len(prefix) :]\n",
+        "\n",
+        "    table = bigquery.TableReference.from_string(bq_table_uri)\n",
+        "\n",
+        "    # Create a BigQuery client\n",
+        "    bqclient = bigquery.Client(project=PROJECT_ID)\n",
+        "\n",
+        "    # Download the table rows\n",
+        "    rows = bqclient.list_rows(\n",
+        "        table,\n",
+        "    )\n",
+        "    return rows.to_dataframe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "SdX_m1Uppkfu"
+      },
+      "outputs": [],
+      "source": [
+        "BQ_SOURCE = \"bq://bigquery-public-data.ml_datasets.penguins\"\n",
+        "\n",
+        "# Download penguins BigQuery table\n",
+        "penguins_df = download_bq_table(BQ_SOURCE)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QuQe6mSbFbhm"
+      },
+      "source": [
+        "### Prepare the data\n",
+        "\n",
+        "Feature values to be written to the Feature Store can take the form of a list of `WriteFeatureValuesPayload` objects, a Python `dict` of the form\n",
+        "\n",
+        "`{entity_id : {feature_id : feature_value}, ...},`\n",
+        "\n",
+        "or a pandas `Dataframe`, where the `index` column holds the unique entity ID strings and each remaining column represents a feature.  In this notebook, since you use a pandas `DataFrame` for ingesting features we convert the index column data type to `string` to be used as `Entity ID`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cljxzJ3bqDer"
+      },
+      "outputs": [],
+      "source": [
+        "# Prepare the data\n",
+        "penguins_df.index = penguins_df.index.map(str)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "GSxrSdSY2ovn"
+      },
+      "outputs": [],
+      "source": [
+        "# Remove null values\n",
+        "NA_VALUES = [\"NA\", \".\"]\n",
+        "penguins_df = penguins_df.replace(to_replace=NA_VALUES, value=np.NaN).dropna()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vgn4oQmSqdKI"
+      },
+      "source": [
+        "## Create Feature Store and define schemas\n",
+        "\n",
+        "Vertex AI Feature Store organizes resources hierarchically in the following order:\n",
+        "\n",
+        "`Featurestore -> EntityType -> Feature`\n",
+        "\n",
+        "You must create these resources before you can ingest data into Vertex AI Feature Store.\n",
+        "\n",
+        "Learn more about [Vertex AI Feature Store](https://cloud.google.com/vertex-ai/docs/featurestore)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "yaHwdbGjZWTq"
+      },
+      "source": [
+        "### Create a Feature Store\n",
+        "\n",
+        "You create a Feature Store using `aiplatform.Featurestore.create` with the following parameters:\n",
+        "\n",
+        "* `featurestore_id (str)`: The ID to use for this Featurestore, which will become the final component of the Featurestore's resource name. The value must be unique within the project and location.\n",
+        "* `online_store_fixed_node_count`: Configuration for online serving resources.\n",
+        "* `project`: Project to create EntityType in. If not set, project set in `aiplatform.init` is used.\n",
+        "* `location`: Location to create EntityType in. If not set, location set in `aiplatform.init` is used.\n",
+        "* `sync`:  Whether to execute this creation synchronously."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cImsONglqfxO"
+      },
+      "outputs": [],
+      "source": [
+        "FEATURESTORE_ID = f\"penguins_{UUID}\"\n",
+        "\n",
+        "penguins_feature_store = aiplatform.Featurestore.create(\n",
+        "    featurestore_id=FEATURESTORE_ID,\n",
+        "    online_store_fixed_node_count=1,\n",
+        "    project=PROJECT_ID,\n",
+        "    location=REGION,\n",
+        "    sync=True,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UfXgSD1VdzKb"
+      },
+      "source": [
+        "##### Verify that the Feature Store is created\n",
+        "Check if the Feature Store was successfully created by running the following code block."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "oud1OdfQd52r"
+      },
+      "outputs": [],
+      "source": [
+        "fs = aiplatform.Featurestore(\n",
+        "    featurestore_name=FEATURESTORE_ID,\n",
+        "    project=PROJECT_ID,\n",
+        "    location=REGION,\n",
+        ")\n",
+        "print(fs.gca_resource)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ep74rSlJWF3c"
+      },
+      "source": [
+        "### Create an EntityType\n",
+        "\n",
+        "An entity type is a collection of semantically related features. You define your own entity types, based on the concepts that are relevant to your use case. For example, a movie service might have the entity types `movie` and `user`, which group related features that correspond to movies or users.\n",
+        "\n",
+        "Here, you create an entity type entity type named `penguin_entity_type` using `create_entity_type` with the following parameters:\n",
+        "* `entity_type_id (str)`: The ID to use for the EntityType, which will become the final component of the EntityType's resource name. The value must be unique within a Feature Store.\n",
+        "* `description`: Description of the EntityType."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "zNzr-FlEr3tI"
+      },
+      "outputs": [],
+      "source": [
+        "ENTITY_TYPE_ID = f\"penguin_entity_type_{UUID}\"\n",
+        "\n",
+        "# Create penguin entity type\n",
+        "penguins_entity_type = penguins_feature_store.create_entity_type(\n",
+        "    entity_type_id=ENTITY_TYPE_ID,\n",
+        "    description=\"Penguins entity type\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "CquSdTp7duVw"
+      },
+      "source": [
+        "##### Verify that the EntityType is created\n",
+        "Check if the Entity Type was successfully created by running the following code block."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "76ocr_hJsG-t"
+      },
+      "outputs": [],
+      "source": [
+        "entity_type = penguins_feature_store.get_entity_type(entity_type_id=ENTITY_TYPE_ID)\n",
+        "\n",
+        "print(entity_type.gca_resource)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2vYV2UUFehwZ"
+      },
+      "source": [
+        "### Create Features\n",
+        "A feature is a measurable property or attribute of an entity type. For example, `penguin` entity type has features such as `flipper_length_mm`, and `body_mass_g`. Features can be created within each entity type.\n",
+        "\n",
+        "When you create a feature, you specify its value type such as `DOUBLE`, and `STRING`. This value determines what value types you can ingest for a particular feature.\n",
+        "\n",
+        "Learn more about [Feature Value Types](https://cloud.google.com/vertex-ai/docs/reference/rest/v1/projects.locations.featurestores.entityTypes.features)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WQ5EsPPbsSuE"
+      },
+      "outputs": [],
+      "source": [
+        "penguins_feature_configs = {\n",
+        "    \"species\": {\n",
+        "        \"value_type\": \"STRING\",\n",
+        "    },\n",
+        "    \"island\": {\n",
+        "        \"value_type\": \"STRING\",\n",
+        "    },\n",
+        "    \"culmen_length_mm\": {\n",
+        "        \"value_type\": \"DOUBLE\",\n",
+        "    },\n",
+        "    \"culmen_depth_mm\": {\n",
+        "        \"value_type\": \"DOUBLE\",\n",
+        "    },\n",
+        "    \"flipper_length_mm\": {\n",
+        "        \"value_type\": \"DOUBLE\",\n",
+        "    },\n",
+        "    \"body_mass_g\": {\"value_type\": \"DOUBLE\"},\n",
+        "    \"sex\": {\"value_type\": \"STRING\"},\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AKRXJCPijM8w"
+      },
+      "source": [
+        "You can create features either using `create_feature` or `batch_create_features`. Here, for convinience, you have added all feature configs in one variabel, so we use `batch_create_features`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "tXOI1Onhs46x"
+      },
+      "outputs": [],
+      "source": [
+        "penguin_features = penguins_entity_type.batch_create_features(\n",
+        "    feature_configs=penguins_feature_configs,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WBx26pZItUN4"
+      },
+      "source": [
+        "### Write features to the Feature Store\n",
+        "Use the `write_feature_values` API to write a feature to the Feature Store with the following parameter:\n",
+        "\n",
+        "* `instances`: Feature values to be written to the Feature Store that can take the form of a list of WriteFeatureValuesPayload objects, a Python dict, or a pandas Dataframe.\n",
+        "\n",
+        "This streaming ingestion feature has been introduced to the Vertex AI SDK under the **preview** namespace. Here, you pass the pandas `Dataframe` you created from penguins dataset as `instances` parameter.\n",
+        "\n",
+        "Learn more about [Streaming ingestion API](https://github.com/googleapis/python-aiplatform/blob/e6933503d2d3a0f8a8f7ef8c178ed50a69ac2268/google/cloud/aiplatform/preview/featurestore/entity_type.py#L36)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "iUGI-ftltXqE"
+      },
+      "outputs": [],
+      "source": [
+        "penguins_entity_type.preview.write_feature_values(instances=penguins_df)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "STq67KHO3q_e"
+      },
+      "source": [
+        "## Read back written features\n",
+        "\n",
+        "Wait a few seconds for the write to propagate, then do an online read to confirm the write was successful."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "lwoMnze43r9G"
+      },
+      "outputs": [],
+      "source": [
+        "ENTITY_IDS = [str(x) for x in range(100)]\n",
+        "penguins_entity_type.read(entity_ids=ENTITY_IDS)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "TpV-iwP9qw9c"
+      },
+      "source": [
+        "## Cleaning up\n",
+        "\n",
+        "To clean up all Google Cloud resources used in this project, you can [delete the Google Cloud\n",
+        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
+        "\n",
+        "Otherwise, you can delete the individual resources you created in this tutorial:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "sx_vKniMq9ZX"
+      },
+      "outputs": [],
+      "source": [
+        "penguins_feature_store.delete(force=True)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "feature_store_streaming_ingestion_sdk.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
@@ -1,7 +1,6 @@
-## Vertex-AI: Matching Engine Notebook
-
-<a id="sdk_matching_engine_for_indexing"></a>[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
+[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)

+```
 Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index.

 The steps performed include:
@@ -12,20 +11,39 @@ The steps performed include:
 * Perform online query
 * Compute recall

-    <details>
-    <summary>Example code snippet from the Notebook:</summary>
-    
-    * Create an IndexEndpoint with VPC Network
-        ```python
-        # [START aiplatform_sdk_matching_engine_for_indexing]
-        VPC_NETWORK = "[your-network-name]"
-        VPC_NETWORK_FULL = "projects/{}/global/networks/{}".format(PROJECT_NUMBER, VPC_NETWORK)
-        my_index_endpoint = aiplatform.MatchingEngineIndexEndpoint.create(
-            display_name="index_endpoint_for_demo",
-            description="index endpoint description",
-            network=VPC_NETWORK_FULL,
-        )
-        # [END aiplatform_sdk_matching_engine_for_indexing]
-        ```
-        [:notebook: sdk_matching_engine_for_indexing.ipynb](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
-    </details>
+```
+
+
+[Introduction to builtin Swivel embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/intro-swivel.ipynb)
+
+```
+Learn how to train custom embeddings using Vertex AI Pipelines and deploy the model for serving.
+
+The steps performed include:
+
+1. **Setup**: Importing the required libraries and setting your global variables.
+2. **Configure parameters**: Setting the appropriate parameter values for the pipeline job.
+3. **Train on Vertex AI Pipelines**: Create a Swivel job to Vertex Pipelines using pipeline template.
+4. **Deploy on Vertex AI Prediction**: Importing and deploying the trained model to a callable endpoint.
+5. **Predict**: Calling the deployed endpoint using online prediction.
+6. **Cleaning up**: Deleting resources created by this tutorial.
+
+```
+
+
+[Introduction to builtin Two-towers embedding algorithm](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/two-tower-model-introduction.ipynb)
+
+```
+Learn how to run the two-tower model.
+
+The steps performed include:
+1. **Setup**: Importing the required libraries and setting your global variables.
+2. **Configure parameters**: Setting the appropriate parameter values for the training job.
+3. **Train on Vertex AI Training**: Submitting a training job.
+4. **Deploy on Vertex AI Prediction**: Importing and deploying the trained model to a callable endpoint.
+5. **Predict**: Calling the deployed endpoint using online or batch prediction.
+6. **Hyperparameter tuning**: Running a hyperparameter tuning job.
+7. **Cleaning up**: Deleting resources created by this tutorial.
+
+```
+
@@ -81,6 +81,12 @@
        "\n",
        "In this notebook, you learn how to train custom embeddings using Vertex AI Pipelines and deploy the model for serving. \n",
        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Pipelines`\n",
+        "- `Vertex AI Training`\n",
+        "- `Swivel builtin algorithm`\n",
+        "\n",
        "The steps performed include:\n",
        "\n",
        "1. **Setup**: Importing the required libraries and setting your global variables.\n",
@@ -60,7 +60,7 @@
      "source": [
        "## Overview\n",
        "\n",
-        "This example demonstrates how to use the GCP ANN Service. It is a high scale, low latency solution, to find similar vectors (or more specifically \"embeddings\") for a large corpus. Moreover, it is a fully managed offering, further reducing operational overhead. It is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research."
+        "This example demonstrates how to use the Vertex AI ANN Service. It is a high scale, low latency solution, to find similar vectors (or more specifically \"embeddings\") for a large corpus. Moreover, it is a fully managed offering, further reducing operational overhead. It is built upon [Approximate Nearest Neighbor (ANN) technology](https://ai.googleblog.com/2020/07/announcing-scann-efficient-vector.html) developed by Google Research."
      ]
    },
    {
@@ -73,6 +73,10 @@
        "\n",
        "In this notebook, you learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index. \n",
        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Matching Engine`\n",
+        "\n",
        "The steps performed include:\n",
        "\n",
        "* Create ANN Index and Brute Force Index\n",
@@ -29,7 +29,7 @@
        "id": "JAPoU8Sm5E6e"
      },
      "source": [
-        "# Introduction to builtin Two-towers embedding algorithm\n",
+        "# Introduction to builtin Two-Towers embedding algorithm\n",
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
@@ -73,7 +73,13 @@
      "source": [
        "### Objective\n",
        "\n",
-        "In this notebook, you learn how to run the two-tower model.\n",
+        "In this notebook, you learn how to run the Two-Tower model.\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services:\n",
+        "\n",
+        "- `Vertex AI Training`\n",
+        "- `Vertex AI Prediction`\n",
+        "- `Two-Tower builtin algorithm`\n",
        "\n",
        "The steps performed include:\n",
        "1. **Setup**: Importing the required libraries and setting your global variables.\n",
@@ -0,0 +1,88 @@
+[Custom Image Classification w/custom training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb)
+
+```
+Learn how to train a tensorflow image classification model using a custom container and Vertex AI training.
+
+The steps performed include:
+
+- *Package the training code into a python application.*
+- *Containerize the training application using Cloud Build and Artifact Registry.*
+- *Create a custom container training job in Vertex AI and run it.*
+- *Evaluate the model generated from the training job.*
+- *Create a model resource for the trained model in Vertex AI Model Registry.*
+- *Run a Vertex AI batch prediction job.*
+- *Deploy the model resource to a Vertex AI Endpoint.*
+- *Run a online prediction job on the model resource.*
+- *Clean up the resources created.*
+
+```
+
+
+[AutoML Tabular Binary Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb)
+
+```
+In this tutorial, you create an AutoML tabular binary classification model and deploy for online prediction from a Python script using the Vertex AI SDK.
+
+The steps performed include:
+
+- Create a Vertex `Dataset` resource.
+- Train the model.
+- View the model evaluation.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction.
+- Undeploy the `Model`
+
+```
+
+
+[Custom Scikit-Learn model with pre-built training container](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb)
+
+```
+Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a scikit-learn model.
+- Upload the trained model artifacts as a `Model` resource.
+- Make a batch prediction.
+- Deploy model to a endpoint
+- Make a online prediction
+
+```
+
+
+[AutoML Text Entity Extraction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb)
+
+```
+The objective of this notebook is to build a AutoML Text Entity Extrasction Model.
+
+The steps performed include the following:
+
+* Set your task name, and GCS prefix
+* Copy AutoML video demo train data for creating managed dataset
+* Create a dataset on Vertex AI.
+* Configure a training job
+* Launch a training job and create a model on Vertex AI
+* Copy AutoML Video Demo Prediction Data for creating batch prediction job
+* Perform batch prediction job on the model
+
+```
+
+
+[AutoML Text Classification](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb)
+
+```
+The objective of this notebook is to build a AutoML Video Classification Model.
+
+The steps performed include the following:
+
+* Set your task name, and GCS prefix
+* Copy AutoML video demo train data for creating managed dataset
+* Create a dataset on Vertex AI.
+* Configure a training job
+* Launch a training job and create a model on Vertex AI
+* Copy AutoML Video Demo Prediction Data for creating batch prediction job
+* Perform batch prediction job on the model
+
+```
+
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ1 Vertex SDK AutoML Image Classification.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10%20Vertex%20SDK%20Custom%20Scikit-Learn%20with%20pre-built%20training%20container.ipynb\">\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10%20Vertex%20SDK%20Custom%20Scikit-Learn%20with%20pre-built%20training%20container.ipynb\">\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "     <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ10%20Vertex%20SDK%20Custom%20Scikit-Learn%20with%20pre-built%20training%20container.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ10 Vertex SDK Custom Scikit-Learn with pre-built training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ14 Vertex SDK AutoML Video Classification.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ15 Vertex SDK AutoML Object Tracking.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ2,12 Vertex SDK Custom Image Classification with pre-built training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -34,18 +34,18 @@
        "<table align=\"left\">\n",
        "\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3%20Vertex%20SDK%20Custom%20Image%20Classification%20with%20custom%20training%20container.ipynb\">\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3%20Vertex%20SDK%20Custom%20Image%20Classification%20with%20custom%20training%20container.ipynb\">\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ3%20Vertex%20SDK%20Custom%20Image%20Classification%20with%20custom%20training%20container.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ3 Vertex SDK Custom Image Classification with custom training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ6%20Vertex%20SDK%20AutoML%20Text%20Classification.ipynb\">\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ6%20Vertex%20SDK%20AutoML%20Text%20Classification.ipynb\">\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "      <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ6%20Vertex%20SDK%20AutoML%20Text%20Classification.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ6 Vertex SDK AutoML Text Classification.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -33,18 +33,18 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ7%20Vertex%20SDK%20AutoML%20Text%20Entity%20Extraction.ipynb\">\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ7%20Vertex%20SDK%20AutoML%20Text%20Entity%20Extraction.ipynb\">\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
        "         <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ7%20Vertex%20SDK%20AutoML%20Text%20Entity%20Extraction.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ7 Vertex SDK AutoML Text Entity Extraction.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -47,7 +47,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ8 Vertex SDK AutoML Text Sentiment Analysis.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
@@ -33,16 +33,22 @@
        "\n",
        "<table align=\"left\">\n",
        "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ9%20Vertex%20SDK%20Custom%20XGBoost%20with%20pre-built%20training%20container.ipynb\">\n",
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Colab logo\"> Run in Colab\n",
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ9%20Vertex%20SDK%20Custom%20XGBoost%20with%20pre-built%20training%20container.ipynb\">\n",
+        "<a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb\" target='_blank'>\n",
        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\">\n",
        "      View on GitHub\n",
        "    </a>\n",
        "  </td>\n",
+        "     <td>\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/migration/UJ9 Vertex SDK Custom XGBoost with pre-built training container.ipynb\" target='_blank'>\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
+        "      Open in Vertex AI Workbench\n",
+        "    </a>\n",
+        "  </td>                      \n",
        "</table>\n",
        "<br/><br/><br/>"
      ]
@@ -1,6 +1,6 @@
-
 [Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)

+```
 Learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.

 The steps performed include:
@@ -8,16 +8,24 @@ The steps performed include:
 - Track parameters and metrics for a locally trained model.
 - Extract and perform analysis for all parameters and metrics within an Experiment.

+```
+
+
 [Track parameters and metrics for custom training jobs](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)

+```
 Learn how to use Vertex AI SDK for Python to:

 The steps performed include:
 - Track training parameters and prediction metrics for a custom training job.
 - Extract and perform analysis for all parameters and metrics within an Experiment.

+```
+
+
 [Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/vertex-pipelines-ml-metadata.ipynb)

+```
 Learn how to track artifacts and metrics with `Vertex ML Metadata` in `Vertex AI Pipeline` runs.

 The steps performed include:
@@ -28,3 +36,6 @@ The steps performed include:
 * Compare Vertex Pipelines runs, both in the Cloud console and programmatically
 * Trace the lineage for pipeline-generated artifacts
 * Query your pipeline run metadata
+
+```
+
@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks//official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "     </a>\n",
@@ -1,32 +1,40 @@
+[Evaluating batch prediction results from an AutoML Tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb)

-[Evaluating BatchPrediction results from AutoML Tabular Classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_classification_model_evaluation.ipynb)
-
-Learn how to train a Vertex AI AutoML Tabular Classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
+```
+Learn how to train a Vertex AI AutoML Tabular classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:

 The steps performed include:

 - Create a Vertex AI `Dataset`.
- Train a Automl Tabular Classification model on the `Dataset` resource.
+- Train an Automl Tabular classification model on the `Dataset` resource.
 - Import the trained `AutoML model resource` into the pipeline.
 - Run a `Batch Prediction` job.
- Evaulate the AutoML model using the `Classification Evaluation Component`.
+- Evaluate the AutoML model using the `Classification Evaluation component`.
 - Import the classification metrics to the AutoML model resource.

-[Evaluating BatchPrediction results from AutoML Tabular Classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)
+```

-Learn how to train a Vertex AI AutoML Tabular Classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:
+
+[Evaluating batch prediction results from AutoML Video classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_video_classification_model_evaluation.ipynb)
+
+```
+Learn how to train a Vertex AI AutoML Video classification model and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`:

 The steps performed include:

- Create a Vertex AI `Dataset`.
- Train a Automl Tabular Classification model on the `Dataset` resource.
- Import the trained `AutoML model resource` into the pipeline.
- Run a `Batch Prediction` job.
- Evaulate the AutoML model using the `Classification Evaluation Component`.
- Import the classification metrics to the AutoML model resource.
+- Create a `Vertex AI Dataset`.
+- Train a Automl Video Classification model on the `Vertex AI Dataset` resource.
+- Import the trained `AutoML Vertex AI Model resource` into the pipeline.
+- Run a batch prediction job inside the pipeline.
+- Evaulate the AutoML model using the classification evaluation component.
+- Import the classification metrics to the AutoML Vertex AI Model resource.

-[Evaluating BatchPrediction results from AutoML Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)
+```

+
+[Evaluating batch prediction results from AutoML Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_tabular_regression_model_evaluation.ipynb)
+
+```
 Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:

 The steps performed include:
@@ -35,12 +43,16 @@ The steps performed include:
 - Configure a `AutoMLTabularTrainingJob`
 - Run the `AutoMLTabularTrainingJob` which returns a model
 - Import a pre-trained `AutoML model resource` into the pipeline
- Run a `batch prediction` job
+- Run a `batch prediction` job in the pipeline
 - Evaulate the AutoML model using the `regression evaluation component`
- Import the Classification Metrics to the AutoML model resource
+- Import the Regression Metrics to the AutoML model resource

-[Evaluating Batch Prediction results from Custom Tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)
+```

+
+[Evaluating batch prediction results from custom tabular regression model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb)
+
+```
 Learn how to evaluate a Vertex AI model resource through a Vertex AI pipeline job using `google_cloud_pipeline_components`:

 The steps performed include:
@@ -50,13 +62,17 @@ The steps performed include:
 - Retrieve and load the model artifacts.
 - View the model evaluation.
 - Upload the model as a Vertex AI Model resource.
- Import a pre-trained `Vertex AI model resource` into the pipeline
- Run a `batch prediction` job
- Evaulate the model using the `regression evaluation component`
- Import the Classification Metrics to the Vertex AI model resource
+- Import a pre-trained `Vertex AI model resource` into the pipeline.
+- Run a `batch prediction` job in the pipeline.
+- Evaulate the model using the `regression evaluation component`.
+- Import the Regression Metrics to the Vertex AI model resource.
+
+```
+

 [AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/automl_text_classification_model_evaluation.ipynb)

+```
 Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.

 The steps performed include:
@@ -68,4 +84,28 @@ The steps performed include:
 - Evaulate the AutoML model using the `Classification Evaluation Component`.
 - Import the classification metrics to the AutoML model resource.

+```
+
+
+[Evaluating BatchPrediction results from a Custom Tabular classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb)
+
+```
+In this tutorial, you train a scikit-learn RandomForest model, save it in Vertex AI Model Registry and learn how to evaluate it through a Vertex AI pipeline job using `google_cloud_pipeline_components`.
+
+The steps performed include:
+
+- Fetch the dataset from the public source.
+- Preprocess the data locally and save test data in BigQuery.
+- Train a RandomForest classification model locally using scikit-learn Python package.
+- Create a custom container in Artifact Registry for predictions.
+- Upload the model in Vertex AI Model Registry.
+- Create and run a Vertex AI Pipeline that:
+    - Imports the trained model into the pipeline.
+    - Runs a `Batch Prediction` job on the test data in BigQuery.
+    - Evaulates the model using the evaluation component from google-cloud-pipeline-components Python SDK.
+    - Imports the classification metrics in to the model resource in Vertex AI Model Registry.
+- Print and visualize the classification evaluation metrics.
+- Clean up the resources created in this notebook.
+
+```

@@ -45,7 +45,7 @@
        "    </a>\n",
        "  </td>\n",
        "  <td>\n",
-        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb\">\n",
+        "<a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/model_evaluation/custom_tabular_classification_model_evaluation.ipynb\" target='_blank'>\n",
        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\">\n",
        "      Open in Vertex AI Workbench\n",
        "    </a>\n",
--- a/Show More
+++ b/Show More
Author	SHA1	Message	Date
Andrew Ferlitsch	080991c5b6	debug: set explicit timeout on operation.result()	2022-12-17 01:38:07 +00:00
Andrew Ferlitsch	e6cd8ecdf9	debug: add more stacktrace	2022-12-17 01:10:46 +00:00
Andrew Ferlitsch	dd9fed55bd	debug: hardcode cli call	2022-12-17 00:59:42 +00:00
Andrew Ferlitsch	5ff5ccba91	debug: explicit pass timeout	2022-12-17 00:34:53 +00:00
Andrew Ferlitsch	ee119f9985	debug: set timeout < 900	2022-12-16 23:45:43 +00:00
Andrew Ferlitsch	b9d226b7e4	debug: add traceback	2022-12-16 23:19:06 +00:00
Andrew Ferlitsch	dfaf49dce1	debug: add traceback	2022-12-16 22:53:16 +00:00
Andrew Ferlitsch	de06e6b47a	Merge branch 'timeout_debug' of https://github.com/GoogleCloudPlatform/vertex-ai-samples into timeout_debug	2022-12-16 22:01:43 +00:00
Andrew Ferlitsch	f6bc7f41d1	debug: backout Dec 1 changes to CI	2022-12-16 22:01:10 +00:00
gericdongandGitHub	4b81238dc4	Removed cleanup changes for testing. (#1364 )	2022-12-16 16:38:28 -05:00
Andrew Ferlitsch	4f07604312	debug: hardcode timeout for 20mins	2022-12-16 21:16:53 +00:00
Andrew Ferlitsch	c58b3654e5	debug: backout protobuf update from Nov 30	2022-12-16 20:42:21 +00:00
Andrew Ferlitsch	bb379c14bf	debug: cloud build timeout	2022-12-16 19:05:28 +00:00
Andrew Ferlitsch	71968c666b	debug: timeout	2022-12-16 18:29:32 +00:00
Andrew Ferlitsch	34a2cd51a0	debug: timeout values	2022-12-16 17:57:13 +00:00
Andrew Ferlitsch	844fd50e0d	debug: will remove	2022-12-15 20:06:29 +00:00
Andrew Ferlitsch	4ebd2319ec	debug: will remove	2022-12-15 20:04:21 +00:00
Andrew FerlitschandGitHub	5f41599745	Autoindex 1 (#1354 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective	2022-12-14 14:11:12 -08:00
Andrew FerlitschandGitHub	67fbd84832	Autoindex 1 (#1353 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective	2022-12-14 12:53:09 -08:00
Andrew FerlitschandGitHub	9b427b6a1f	Autoindex 1 (#1352 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective	2022-12-14 12:45:39 -08:00
Andrew FerlitschandGitHub	37d5d5b992	Autoindex 1 (#1351 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective	2022-12-14 11:26:16 -08:00
Andrew FerlitschandGitHub	287911b681	Autoindex 1 (#1350 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective * fix: branding and objective * fix: branding and objective * fix: branding and objective	2022-12-14 11:20:14 -08:00
Andrew FerlitschandGitHub	c83387181a	Autoindex 1 (#1349 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective * fix: branding and objective * fix: branding and objective	2022-12-14 11:13:18 -08:00
Andrew FerlitschandGitHub	236d45b87e	Autoindex 1 (#1348 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder * fix: branding and objective	2022-12-14 10:50:01 -08:00
Soheila ZangenehandGitHub	4eb7b3ce39	Feature Store ingestion streaming notebook (#1321 ) * Add featurestore ingestion streaming nb * Add notebook to CODEOWNERS * Run linter * Add pyarrow installation * Run linter * Resolve PR comments * Run linter	2022-12-14 10:47:46 -08:00
Rajesh ThallamandGitHub	d74554f641	Torchrun notebook (#1344 ) * PyTorch efficient training - refcator code * Revert "PyTorch efficient training - refcator code" This reverts commit 90b563a7697b15b4154ac76236b894253dd58f3c. * Refactor torchrun notebook * Refactor torchrun notebook * Refactor torchrun notebook * Torchrun notebook - Linting fixes * Torchrun notebook - Linting fixes	2022-12-13 10:23:58 -08:00
Andrew FerlitschandGitHub	3e70c63899	Autoindex 1 (#1343 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance * fix: autogen README index for workbench folder	2022-12-13 09:44:58 -08:00
Andrew FerlitschandGitHub	4c79ab91e2	Autoindex 1 (#1342 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder * fix: template conformance	2022-12-13 09:24:15 -08:00
Andrew FerlitschandGitHub	dd8a7ad325	Autoindex 1 (#1341 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes * fix: bad links in workbench folder	2022-12-13 09:12:30 -08:00
Andrew FerlitschandGitHub	a3bb273e78	Autoindex 1 (#1340 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes * fix: update official indexes	2022-12-12 19:04:27 -08:00
Andrew FerlitschandGitHub	1ff0872546	Autoindex 1 (#1339 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official * fix: update indexes	2022-12-12 18:56:26 -08:00
Andrew FerlitschandGitHub	91144b8476	Autoindex 1 (#1338 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices * fix: update autogen index in official	2022-12-12 18:40:26 -08:00
Andrew FerlitschandGitHub	9822bd64a1	Autoindex 1 (#1337 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices * fix: update official indices	2022-12-12 16:47:05 -08:00
Andrew FerlitschandGitHub	f14ff50d2b	Autoindex 1 (#1336 ) * feat: autogen index * feat: autogen index * feat: autogen index * feat: update indices	2022-12-12 16:27:49 -08:00
Andrew FerlitschandGitHub	fdc30dab67	Autoindex 1 (#1335 ) * feat: autogen index * feat: autogen index * feat: autogen index	2022-12-12 16:12:05 -08:00
Andrew FerlitschandGitHub	93229f62c9	fix: next round of restructuring. (#1163 ) * fix: working on abstract class * fix: working on abstract class * fix: restructuring * fix: changes per TW needs * fix: request changes * fix: before you begin * feat: task: making cell navigation independent of rules * fix: review comments * fix: review comments * fix: review comments * fix: review comments * feat: writeback fixed notebook * fix: target=_blank detection * fix: autofixing bad link	2022-12-12 14:55:10 -08:00
Peter PingandGitHub	b37f474255	Update stream_update_for_matching_engine.ipynb (#1324 ) * Update stream_update_for_matching_engine.ipynb change "allow_list" to "allow" for index creation as allow_list is not supported but allow is supported for index creation. * Update stream_update_for_matching_engine.ipynb Updated to resolve the comments. * Updated Google Cloud Notebooks to Workbench AI Notebooks	2022-12-12 09:29:38 -08:00
Ivan NardiniandGitHub	344b0dd6d7	update vertex_ai_model_registry_bqml_custom_model_versioning.ipynb (#1331 ) * fix dataproc version issue * linter test passed	2022-12-12 09:12:35 -08:00
Andrew FerlitschandGitHub	48b7cdb21d	Ci admin howto 2 (#1333 ) * feat: howto admin * fix: review comments	2022-12-12 08:40:45 -08:00
Andrew FerlitschandGitHub	cefdc32d5f	feat: howto admin (#1330 )	2022-12-09 14:52:36 -08:00
Andrew FerlitschandGitHub	7558411fbc	feat: migrate batch model monitoring notebook to official (#1322 ) * feat: migrate notebook to official * fix: set links to official * fix package issue when testing * Update batch_prediction_model_monitoring.ipynb * Update batch_prediction_model_monitoring.ipynb * Update batch_prediction_model_monitoring.ipynb * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: install/import issues * fix: dependencies * fix: missing import	2022-12-09 14:38:33 -08:00
Andrew FerlitschandGitHub	1c0487c459	fix: rm line about load mean/stddev (#1327 )	2022-12-08 15:04:56 -08:00
Edgar BermudezandGitHub	6b7bb867ab	Fixed links to notebooks (#1302 ) * Update README.md * Fixed links to notebooks * Fixed links to notebooks	2022-12-07 16:58:42 -08:00
Alexey VolkovandGitHub	8c7363c2b0	Updated the tabular training pipelines to load the components from the vertex-ai-samples repo (#1326 )	2022-12-07 16:54:34 -08:00
Brian KangandGitHub	2ea22da2bf	Briankang pytorch torchrun (#1323 ) * Adding PyTorch Torchrun example * Revert 'Adding PyTorch Torchrun example' This reverts commit `239b9fe3b7` * Adding PyTorch torchrun ImageNet training example * Updated CODEOWNERS for PyTorch torchrun example * Ran Linter * Updated based on review feedback	2022-12-07 09:52:49 -08:00
Soheila ZangenehandGitHub	1d96584421	Notebook to demonstrate feature filtering in BatchPredictionJob (#1309 ) * Add feature filter notebook * Clean code * Run linter * Add the notebook to CODEOWNERS * Remove user flag * Run linter * Fix bucket URI * Run linter * Simplify the notebook	2022-12-07 09:49:29 -08:00
Ivan NardiniandGitHub	06d57da90c	inardini -- anomaly detection with BigQuery ML and Vertex AI (#1319 ) * adding anomalydetection pipeline * reviewed notebook * add code owner * remove to do * linter test * reviewed based on feeback from andy * linter test passed * fix links * linter test passed	2022-12-07 09:42:34 -08:00
Alexey VolkovandGitHub	355105218d	Added pipeline components used in the "Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines" samples (#1294 ) * Added pipeline components used in the "Train_tabular_models_with_many_frameworks_and_import_to_Vertex_AI_using_Pipelines" samples. * Removed Pandas type conversion * Removed Pandas type conversion	2022-12-06 16:42:06 -08:00
Andrew FerlitschandGitHub	3090a312c0	fix: autofix bad links (#1318 ) * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links * fix: autofix bad links	2022-12-06 14:17:41 -08:00
Ivan Cheung GitHub Andrew Ferlitsch	8c265ee1ef	Added matching engine resource managers (#1314 ) Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-12-01 13:41:23 -08:00
sudarshan-SpringML GitHub Andrew Ferlitsch	fdbfecda04	Updates notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb (#1264 ) * remove key_columns variable * ran linter * remove key_columns in text * notebooks/official/model_evaluation/custom_tabular_regression_model_evaluation.ipynb * modified text * ran linter * andrew comments resolved * ran linter Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>	2022-12-01 09:30:42 -08:00
uday kumar GitHub Andrew Ferlitsch Ivan Cheung	7050730b6a	sdk_automl_tabular_regression_online_bq (#1246 ) * bioler plate changes * linter test * made review changes * linter test * bioler plate changes * linter test Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com> Co-authored-by: Ivan Cheung <ivans.mailbox@gmail.com>	2022-12-01 05:04:26 -08:00
Ivan CheungandGitHub	4ec962d277	custom-tabular-bq-managed-dataset.ipynb: Addressed comments regarding evaluation, normalization and dataset creation (#1313 ) * Address comments * Added back BQ dataset creation	2022-11-30 16:09:39 -05:00
Ivan CheungandGitHub	3185ca7d48	Hierarchical forecasting notebook (#664 ) This is a hierarchical forecasting notebook demonstrating the use of the new hierarchical forecasting parameters for AutoML Forecasting. If you are opening a PR for `Official Notebooks` under the [notebooks/official](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/notebooks/official) folder, follow this mandatory checklist: - [x] Use the [notebook template](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/notebook_template.ipynb) as a starting point. - [x] Follow the style and grammar rules outlined in the above notebook template. - [ ] Verify the notebook runs successfully in Colab since the automated tests cannot guarantee this even when it passes. - [x] Passes all the required automated checks. You can locally test for formatting and linting with these [instructions](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/CONTRIBUTING.md#code-quality-checks). - [ ] You have consulted with a tech writer to see if tech writer review is necessary. If so, the notebook has been reviewed by a tech writer, and they have approved it. - [x] This notebook has been added to the [CODEOWNERS](https://togithub.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/CODEOWNERS) file under the `Official Notebooks` section, pointing to the author or the author's team. - [x] The Jupyter notebook cleans up any artifacts it has created (datasets, ML models, endpoints, etc) so as not to eat up unnecessary resources.	2022-11-30 20:02:13 +00:00