Merge branch 'main' into automl_tabular_batch_explain

Co-authored-by: Andrew Ferlitsch <aferlitsch@google.com>

.github/workflows/linter/Dockerfile

@@ -0,0 +1,20 @@
+# To use this image, run this command with the desired notebook args from the top-level vertex-ai-samples directory:
+# 1. To lint all changed notebooks:
+# docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest
+# 2. To lint specific notebooks:
+# docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest notebooks/1.ipynb notebooks/2.ipynb
+
+FROM python:3.10
+
+WORKDIR setup
+
+COPY ./requirements.txt .
+COPY ./run_linter.sh .
+
+# Install dependencies.
+RUN pip install --upgrade pip
+RUN pip install -r requirements.txt
+
+WORKDIR app
+
+ENTRYPOINT ["/setup/run_linter.sh"]

.github/workflows/linter/run_linter.sh

@@ -47,12 +47,22 @@ done
 
 echo "Test mode: $is_test"
 
+# Read in user-provided notebooks
+notebooks=()
+for arg in "$@"; do
+    if [[ $arg == *.ipynb ]]; then
+        notebooks+=("$arg")
+    fi
+done
+
 # Only check notebooks in test folders modified in this pull request.
 # Note: Use process substitution to persist the data in the array
-notebooks=()
-while read -r file || [ -n "$line" ]; do
-    notebooks+=("$file")
-done < <(git diff --name-only main... | grep '\.ipynb$')
+if [ ${#notebooks[@]} -eq 0 ]; then
+    echo "Checking for changed notebooked using git"
+    while read -r file || [ -n "$line" ]; do
+        notebooks+=("$file")
+    done < <(git diff --name-only main... | grep '\.ipynb$')
+fi
 
 problematic_notebooks=()
 if [ ${#notebooks[@]} -gt 0 ]; then

community-content/CODEOWNERS

@@ -1,5 +1,6 @@
 *  @vertex-ai-samples-contributors  @GoogleCloudPlatform/cloudml-samples-owners
 /tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk	@yinghsienwu
+/pytorch_pre_built_images_deployment @googleapis/vertex-prediction-team
 /pytorch_text_classification_using_vertex_sdk_and_gcloud  @RajeshThallam
 /pytorch_text_classification_using_vertex_sdk_and_gcloud		@RajeshThallam @ultrons
 /sklearn_text_classification_from_script_using_vertex_sdk       @maxhardt

community-content/cpr-examples/timm_serving/.gitignore

@@ -2,4 +2,5 @@ cpr_model_server.py
 entrypoint.py
 state_dict.pth
 config.json
-**/__pycache__
+**/__pycache__
+!testdata/**

community-content/cpr-examples/timm_serving/README.md

@@ -2,7 +2,7 @@
 
 ## About CPR
 
-CPR ([custom prediction routines](https://github.com/googleapis/python-aiplatform/blob/custom-prediction-routine/google/cloud/aiplatform/prediction/README.md)) is a framework designed by Google Cloud developers to make it easier to combine machine learning models with custom preprocessing and postprocessing logic in a real-time serving application. 
+CPR ([custom prediction routines](https://github.com/googleapis/python-aiplatform/blob/main/google/cloud/aiplatform/prediction/README.md)) is a framework designed by Google Cloud developers to make it easier to combine machine learning models with custom preprocessing and postprocessing logic in a real-time serving application. 
 
 ## Using this example
 
@@ -34,6 +34,23 @@ Finally, install the Python modules required to build and run the model server:
 pip install -r requirements.txt
 ```
 
+### Auth
+
+This example uses Google Cloud Storage for hosting model artifacts and Artifact Registry to store the container image. 
+You'll need to authorize yourself before you can interact with these.
+
+First, log in to GCP with application default credentials:
+```sh
+gcloud auth application-default login
+```
+
+Next, if you haven't done so already, set up the [gcloud credential helper](https://cloud.google.com/artifact-registry/docs/docker/authentication)
+for the Artifact Registry region where you intend to host the image.  
+```
+gcloud auth configure-docker <region>-docker.pkg.dev
+```
+
+
 ### Predictor
 
 The `TimmPredictor` class in `timm_serving/predictor.py` implements most of the important logic for the server.

community-content/cpr-examples/timm_serving/config.py

@@ -60,9 +60,9 @@ class CPRConfig(object):
     image: str = "timm_predictor:latest"
     artifact_local_dir: str = ""
     region: str = "us-central1"
-    project_id: str = "samthrasher-experimental"
+    project_id: str = "<your project ID here>"
     repository: str = "cpr-images"
-    artifact_gcs_dir: str = "gs://samthrasher-cpr-example/timm-vit224/"
+    artifact_gcs_dir: str = "gs://<your bucket ID here>/timm-vit224/"
     model_name: str = ""
     endpoint_name: str = ""
     machine_type: str = "n1-standard-2"

community-content/cpr-examples/timm_serving/requirements.txt

@@ -5,4 +5,4 @@ timm==0.5.4
 smart_open==6.0.0
 
 google-cloud-storage>=1.26.0,<2.0.0dev
-google-cloud-aiplatform[prediction] @ git+https://github.com/googleapis/python-aiplatform.git@custom-prediction-routine
+google-cloud-aiplatform[prediction]>=1.16.0

community-content/cpr-examples/timm_serving/test.py

@@ -70,7 +70,10 @@ class PredictorUnitTests(absltest.TestCase):
     def setUp(self):
         super().setUp()
         self.config = CPRConfig()
-        self.config.load()
+        try:
+            self.config.load()
+        except FileNotFoundError:
+            logging.info("No saved config file found, using default values.")
         self.predictor = predictor.TimmPredictor()
 
     def test_load_from_saved_state_dict_ok(self):
@@ -170,7 +173,10 @@ class ServerEndToEndTests(absltest.TestCase):
     def setUp(self):
         super().setUp()
         self.config = CPRConfig()
-        self.config.load()
+        try:
+            self.config.load()
+        except FileNotFoundError:
+            logging.info("No saved config file found, using default values.")
         self.local_model = cpr.LocalModel(
             serving_container_spec=aiplatform.gapic.ModelContainerSpec(
                 image_uri=self.config.image

community-content/cpr-examples/timm_serving/testdata/bad_model_1/state_dict.pth

@@ -0,0 +1 @@
+blah

community-content/pytorch_pre_built_images_deployment/README.md

@@ -0,0 +1,30 @@
+# PyTorch Deployment on Google Cloud: Text Classification
+
+**This is an Experimental release**, covered by the Pre-GA Offerings Terms of your Google Cloud Platform [Terms of Service](https://cloud.google.com/terms).
+
+Experiments are focused on validating a prototype and are not guaranteed to be released. They are not intended for production use or covered by any SLA, support obligation, or deprecation policy and might be subject to backward-incompatible changes.
+
+**Kindly drop us a note before you run any scale tests.**
+
+**Do not hesitate to contact vertexai-prediction-preview-feedback@google.com if you have any questions or run into any issues.**
+
+The projects need to be added to the allowlist in order to deploy PyTorch models using Vertex AI Prediction pre-built PyTorch images. If you are interested in the feature, please send an email to vertexai-prediction-preview-feedback@google.com to provide your project numbers OR project ids.
+
+## Overview
+
+In the PyTorch on Google Cloud series of blog posts, we aim to share how to deploy PyTorch models at scale on [Vertex AI](https://cloud.google.com/vertex-ai).
+
+This tutorial on text classification shows how to deploy a PyTorch based text classification model on [Vertex AI](https://cloud.google.com/vertex-ai/docs/start/client-libraries#python) using Vertex SDK and [`gcloud ai`](https://cloud.google.com/sdk/gcloud/reference/beta/ai).
+
+## Notebooks
+
+| <h4>Notebook</h4> | <h4>Description</h4>                       |
+| :-------- | :------- |
+| [pytorch-text-classification-vertex-ai-deploy.ipynb](./pytorch-text-classification-vertex-ai-deploy.ipynb) | Notebook to show deploying a PyTorch model on Vertex AI |
+
+## Folders
+
+
+| <h4>Folder Name</h4> | <h4>Description</h4>                       |
+| :-------- | :------- |
+| [`predictor`](./predictor) | Folder with custom prediction handler to deploy a PyTorch model to Vertex Prediction. In the [notebook](./pytorch-text-classification-vertex-ai-deploy.ipynb), this folder is used for deploying a PyTorch model on Vertex AI using Vertex Prediction pre-built PyTorch images |

community-content/pytorch_pre_built_images_deployment/predictor/custom_handler.py

@@ -0,0 +1,91 @@
+
+import os
+import json
+import logging
+
+import torch
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from ts.torch_handler.base_handler import BaseHandler
+
+logger = logging.getLogger(__name__)
+
+
+class TransformersClassifierHandler(BaseHandler):
+    """
+    The handler takes an input string and returns the classification text 
+    based on the serialized transformers checkpoint.
+    """
+    def __init__(self):
+        super(TransformersClassifierHandler, self).__init__()
+        self.initialized = False
+
+    def initialize(self, ctx):
+        """ Loads the model.pt file and initialized the model object.
+        Instantiates Tokenizer for preprocessor to use
+        Loads labels to name mapping file for post-processing inference response
+        """
+        self.manifest = ctx.manifest
+
+        properties = ctx.system_properties
+        model_dir = properties.get("model_dir")
+        self.device = torch.device("cuda:" + str(properties.get("gpu_id")) if torch.cuda.is_available() else "cpu")
+
+        # Read model serialize/pt file
+        serialized_file = self.manifest["model"]["serializedFile"]
+        model_pt_path = os.path.join(model_dir, serialized_file)
+        if not os.path.isfile(model_pt_path):
+            raise RuntimeError("Missing the model.pt or pytorch_model.bin file")
+        
+        # Load model
+        self.model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+        self.model.to(self.device)
+        self.model.eval()
+        logger.debug('Transformer model from path {0} loaded successfully'.format(model_dir))
+        
+        # Ensure to use the same tokenizer used during training
+        self.tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
+
+        # Read the mapping file, index to object name
+        mapping_file_path = os.path.join(model_dir, "index_to_name.json")
+
+        if os.path.isfile(mapping_file_path):
+            with open(mapping_file_path) as f:
+                self.mapping = json.load(f)
+        else:
+            logger.warning('Missing the index_to_name.json file. Inference output will default.')
+            self.mapping = {"0": "Negative",  "1": "Positive"}
+
+        self.initialized = True
+
+    def preprocess(self, data):
+        """ Preprocessing input request by tokenizing
+            Extend with your own preprocessing steps as needed
+        """
+        text = data[0].get("data")
+        if text is None:
+            text = data[0].get("body")
+        sentences = text.decode('utf-8')
+        logger.info("Received text: '%s'", sentences)
+
+        # Tokenize the texts
+        tokenizer_args = ((sentences,))
+        inputs = self.tokenizer(*tokenizer_args,
+                                padding='max_length',
+                                max_length=128,
+                                truncation=True,
+                                return_tensors = "pt")
+        return inputs
+
+    def inference(self, inputs):
+        """ Predict the class of a text using a trained transformer model.
+        """
+        prediction = self.model(inputs['input_ids'].to(self.device))[0].argmax().item()
+
+        if self.mapping:
+            prediction = self.mapping[str(prediction)]
+
+        logger.info("Model predicted: '%s'", prediction)
+        return [prediction]
+
+    def postprocess(self, inference_output):
+        return inference_output

community-content/pytorch_pre_built_images_deployment/predictor/index_to_name.json

@@ -0,0 +1,5 @@
+
+{
+    "0": "Negative", 
+    "1": "Positive"
+}

community-content/pytorch_text_classification_using_vertex_sdk_and_gcloud/pytorch-text-classification-vertex-ai-train-tune-deploy.ipynb

@@ -658,8 +658,8 @@
       },
       "outputs": [],
       "source": [
-        "datasets = load_dataset(\"imdb\")\n",
-        "datasets"
+        "dataset = load_dataset(\"imdb\")\n",
+        "dataset"
       ]
     },
     {
@@ -668,7 +668,7 @@
         "id": "RzfPtOMoIrIu"
       },
       "source": [
-        "The `datasets` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set."
+        "The `dataset` object itself is [`DatasetDict`](https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasetdict), which contains one key for the training, validation and test set."
       ]
     },
     {
@@ -681,12 +681,12 @@
       "source": [
         "print(\n",
         "    \"Total # of rows in training dataset {} and size {:5.2f} MB\".format(\n",
-        "        datasets[\"train\"].shape[0], datasets[\"train\"].size_in_bytes / (1024 * 1024)\n",
+        "        dataset[\"train\"].shape[0], dataset[\"train\"].size_in_bytes / (1024 * 1024)\n",
         "    )\n",
         ")\n",
         "print(\n",
         "    \"Total # of rows in test dataset {} and size {:5.2f} MB\".format(\n",
-        "        datasets[\"test\"].shape[0], datasets[\"test\"].size_in_bytes / (1024 * 1024)\n",
+        "        dataset[\"test\"].shape[0], dataset[\"test\"].size_in_bytes / (1024 * 1024)\n",
         "    )\n",
         ")"
       ]
@@ -708,7 +708,7 @@
       },
       "outputs": [],
       "source": [
-        "datasets[\"train\"][0]"
+        "dataset[\"train\"][0]"
       ]
     },
     {
@@ -728,7 +728,7 @@
       },
       "outputs": [],
       "source": [
-        "label_list = datasets[\"train\"].unique(\"label\")\n",
+        "label_list = dataset[\"train\"].unique(\"label\")\n",
         "label_list"
       ]
     },
@@ -779,7 +779,7 @@
       },
       "outputs": [],
       "source": [
-        "show_random_elements(datasets[\"train\"])"
+        "show_random_elements(dataset[\"train\"])"
       ]
     },
     {
@@ -883,7 +883,7 @@
       },
       "outputs": [],
       "source": [
-        "example = datasets[\"train\"][4]\n",
+        "example = dataset[\"train\"][4]\n",
         "print(example)"
       ]
     },
@@ -920,7 +920,7 @@
       "source": [
         "# Dataset loading repeated here to make this cell idempotent\n",
         "# Since we are over-writing datasets variable\n",
-        "datasets = load_dataset(\"imdb\")\n",
+        "dataset = load_dataset(\"imdb\")\n",
         "\n",
         "# Mapping labels to ids\n",
         "# NOTE: We can extract this automatically but the `Unique` method of the datasets\n",
@@ -948,7 +948,7 @@
         "\n",
         "\n",
         "# apply preprocessing function to input examples\n",
-        "datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=True)"
+        "dataset = dataset.map(preprocess_function, batched=True, load_from_cache_file=True)"
       ]
     },
     {
@@ -1091,8 +1091,8 @@
         "trainer = Trainer(\n",
         "    model,\n",
         "    args,\n",
-        "    train_dataset=datasets[\"train\"],\n",
-        "    eval_dataset=datasets[\"test\"],\n",
+        "    train_dataset=dataset[\"train\"],\n",
+        "    eval_dataset=dataset[\"test\"],\n",
         "    data_collator=default_data_collator,\n",
         "    tokenizer=tokenizer,\n",
         "    compute_metrics=compute_metrics,\n",

notebooks/community/CODEOWNERS

@@ -17,6 +17,7 @@
 /explainable_ai/SDK_Custom_Container_XAI.ipynb @brianchunkang
 /matching_engine/sdk_matching_engine_for_indexing.ipynb @ivanmkc
 /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
 /tensorboard @yfang1
 /feature_store @nayaknishant @morgandu
@@ -27,4 +28,6 @@
 /notebooks/community/ml_ops/stage2/get_started_with_visionapi_and_automl.ipynb @mansari
 /notebooks/community/neo4j/graph_paysim.ipynb @benofben @laeg
 /notebooks/community/ml_ops/stage1/get_started_with_visionapi_and_vertex_datasets.ipynb @mansari
-/notebooks/community/pipelines/google_cloud_pipeline_components_bqml_pipeline_demand_forecasting.ipynb @inardini
+/notebooks/community/pipelines/google_cloud_pipeline_components_bqml_pipeline_demand_forecasting.ipynb @inardini
+/notebooks/community/ml_ops/stage2/get_started_vertex_hpt_r_kernel.ipynb @fhirschmann
+/notebooks/community/ml_ops/stage2/get_started_vertex_training_r_using_r_kernel.ipynb @fhirschmann

notebooks/community/feature_store/gapic-feature-store.ipynb

@@ -292,6 +292,37 @@
         "    PROJECT_ID = \"python-docs-samples-tests\"  # @param {type:\"string\"}"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d9f118b92c74"
+      },
+      "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": "3ee72715c0fd"
+      },
+      "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": {
@@ -478,7 +509,6 @@
         "from google.cloud.aiplatform_v1.types import \\\n",
         "    featurestore_service as featurestore_service_pb2\n",
         "from google.cloud.aiplatform_v1.types import io as io_pb2\n",
-        "from google.protobuf.duration_pb2 import Duration\n",
         "\n",
         "# Create admin_client for CRUD and data_client for reading feature values.\n",
         "admin_client = FeaturestoreServiceClient(client_options={\"api_endpoint\": API_ENDPOINT})\n",
@@ -542,7 +572,7 @@
       },
       "outputs": [],
       "source": [
-        "FEATURESTORE_ID = \"movie_prediction\"\n",
+        "FEATURESTORE_ID = f\"movie_prediction_{UUID}\"\n",
         "try:\n",
         "    create_lro = admin_client.create_featurestore(\n",
         "        featurestore_service_pb2.CreateFeaturestoreRequest(\n",
@@ -567,7 +597,7 @@
         "id": "ag8pCQ7rNjVf"
       },
       "source": [
-        "You can use [GetFeaturestore](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.GetFeaturestore) or [ListFeaturestores](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeaturestores) to check if the Featurestore was successfully created. The following example gets the details of the Featurestore.\n"
+        "You can use [GetFeaturestore](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.GetFeaturestore) or [ListFeaturestores](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.ListFeaturestores) to check if the Featurestore was successfully created. The following example gets the details of the Featurestore.\n"
       ]
     },
     {
@@ -589,7 +619,7 @@
         "id": "018ab19d934f"
       },
       "source": [
-        "Auto scaling is available in v1beta1 since v1.11. Below is the example for the `CreateFeaturestoreRequest` with auto-scaling, use it with `aiplatform_v1beta1.FeaturestoreServiceClient` to create Featurestore:"
+        "Auto scaling is available in v1 since v1.11. Below is the example for the `CreateFeaturestoreRequest` with auto-scaling, use it with `aiplatform_v1.FeaturestoreServiceClient` to create Featurestore:"
       ]
     },
     {
@@ -600,17 +630,17 @@
       },
       "outputs": [],
       "source": [
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    featurestore as v1beta1_featurestore_pb2\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    featurestore_service as v1beta1_featurestore_service_pb2\n",
+        "from google.cloud.aiplatform_v1.types import \\\n",
+        "    featurestore as v1_featurestore_pb2\n",
+        "from google.cloud.aiplatform_v1.types import \\\n",
+        "    featurestore_service as v1_featurestore_service_pb2\n",
         "\n",
-        "create_featurestore_request = v1beta1_featurestore_service_pb2.CreateFeaturestoreRequest(\n",
+        "create_featurestore_request = v1_featurestore_service_pb2.CreateFeaturestoreRequest(\n",
         "    parent=BASE_RESOURCE_PATH,\n",
         "    featurestore_id=FEATURESTORE_ID,\n",
-        "    featurestore=v1beta1_featurestore_pb2.Featurestore(\n",
-        "        online_serving_config=v1beta1_featurestore_pb2.Featurestore.OnlineServingConfig(\n",
-        "            scaling=v1beta1_featurestore_pb2.Featurestore.OnlineServingConfig.Scaling(\n",
+        "    featurestore=v1_featurestore_pb2.Featurestore(\n",
+        "        online_serving_config=v1_featurestore_pb2.Featurestore.OnlineServingConfig(\n",
+        "            scaling=v1_featurestore_pb2.Featurestore.OnlineServingConfig.Scaling(\n",
         "                min_node_count=1, max_node_count=5\n",
         "            )\n",
         "        ),\n",
@@ -681,7 +711,7 @@
         "id": "dPkT7KDuEvWv"
       },
       "source": [
-        "Feature [monitoring](https://cloud.google.com/vertex-ai/docs/featurestore/monitoring) is in preview, so you need to use v1beta1 Python. Import feature analysis is only available through SDK for now."
+        "Feature [monitoring](https://cloud.google.com/vertex-ai/docs/featurestore/monitoring) is in preview, so you need to use v1 Python. Import feature analysis is only available through SDK for now."
       ]
     },
     {
@@ -692,36 +722,35 @@
       },
       "outputs": [],
       "source": [
-        "from google.cloud.aiplatform_v1beta1 import \\\n",
-        "    FeaturestoreServiceClient as v1beta1_FeaturestoreServiceClient\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    entity_type as v1beta1_entity_type_pb2\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    featurestore_monitoring as v1beta1_featurestore_monitoring_pb2\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    featurestore_service as v1beta1_featurestore_service_pb2\n",
+        "from google.cloud.aiplatform_v1 import \\\n",
+        "    FeaturestoreServiceClient as v1_FeaturestoreServiceClient\n",
+        "from google.cloud.aiplatform_v1.types import entity_type as v1_entity_type_pb2\n",
+        "from google.cloud.aiplatform_v1.types import \\\n",
+        "    featurestore_monitoring as v1_featurestore_monitoring_pb2\n",
+        "from google.cloud.aiplatform_v1.types import \\\n",
+        "    featurestore_service as v1_featurestore_service_pb2\n",
         "\n",
-        "v1beta1_admin_client = v1beta1_FeaturestoreServiceClient(\n",
+        "v1_admin_client = v1_FeaturestoreServiceClient(\n",
         "    client_options={\"api_endpoint\": API_ENDPOINT}\n",
         ")\n",
         "\n",
         "# Enable import feature analysis for users entity type.\n",
         "# All Features belonging to this EntityType will by default inherit the monitoring config.\n",
-        "v1beta1_admin_client.update_entity_type(\n",
-        "    v1beta1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
-        "        entity_type=v1beta1_entity_type_pb2.EntityType(\n",
+        "v1_admin_client.update_entity_type(\n",
+        "    v1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
+        "        entity_type=v1_entity_type_pb2.EntityType(\n",
         "            name=admin_client.entity_type_path(\n",
         "                PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n",
         "            ),\n",
-        "            monitoring_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
-        "                import_features_analysis=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis(\n",
-        "                    anomaly_detection_baseline=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline.LATEST_STATS,\n",
-        "                    state=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State.ENABLED,\n",
+        "            monitoring_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
+        "                import_features_analysis=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis(\n",
+        "                    anomaly_detection_baseline=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.Baseline.LATEST_STATS,\n",
+        "                    state=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ImportFeaturesAnalysis.State.ENABLED,\n",
         "                ),\n",
-        "                numerical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                numerical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
         "                    value=0.001,\n",
         "                ),\n",
-        "                categorical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                categorical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
         "                    value=0.001,\n",
         "                ),\n",
         "            ),\n",
@@ -736,7 +765,7 @@
         "id": "85b1f59fbf6d"
       },
       "source": [
-        "The easiest way to set up snapshot analysis for now is using [console UI](https://console.cloud.google.com/vertex-ai/features). For completeness, below is example to do this using v1beta1 SDK.\n",
+        "The easiest way to set up snapshot analysis for now is using [console UI](https://console.cloud.google.com/vertex-ai/features). For completeness, below is example to do this using v1 SDK.\n",
         "\n",
         "You can view monitoring statistics on [console UI](https://console.cloud.google.com/vertex-ai/features)."
       ]
@@ -749,36 +778,35 @@
       },
       "outputs": [],
       "source": [
-        "from google.cloud.aiplatform_v1beta1 import \\\n",
-        "    FeaturestoreServiceClient as v1beta1_FeaturestoreServiceClient\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    entity_type as v1beta1_entity_type_pb2\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    featurestore_monitoring as v1beta1_featurestore_monitoring_pb2\n",
-        "from google.cloud.aiplatform_v1beta1.types import \\\n",
-        "    featurestore_service as v1beta1_featurestore_service_pb2\n",
+        "from google.cloud.aiplatform_v1 import \\\n",
+        "    FeaturestoreServiceClient as v1_FeaturestoreServiceClient\n",
+        "from google.cloud.aiplatform_v1.types import entity_type as v1_entity_type_pb2\n",
+        "from google.cloud.aiplatform_v1.types import \\\n",
+        "    featurestore_monitoring as v1_featurestore_monitoring_pb2\n",
+        "from google.cloud.aiplatform_v1.types import \\\n",
+        "    featurestore_service as v1_featurestore_service_pb2\n",
         "\n",
-        "v1beta1_admin_client = v1beta1_FeaturestoreServiceClient(\n",
+        "v1_admin_client = v1_FeaturestoreServiceClient(\n",
         "    client_options={\"api_endpoint\": API_ENDPOINT}\n",
         ")\n",
         "\n",
         "# Enable snapshot analysis for users entity type.\n",
         "# All Features belonging to this EntityType will by default inherit the monitoring config.\n",
-        "v1beta1_admin_client.update_entity_type(\n",
-        "    v1beta1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
-        "        entity_type=v1beta1_entity_type_pb2.EntityType(\n",
+        "v1_admin_client.update_entity_type(\n",
+        "    v1_featurestore_service_pb2.UpdateEntityTypeRequest(\n",
+        "        entity_type=v1_entity_type_pb2.EntityType(\n",
         "            name=admin_client.entity_type_path(\n",
         "                PROJECT_ID, REGION, FEATURESTORE_ID, \"users\"\n",
         "            ),\n",
-        "            monitoring_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
-        "                snapshot_analysis=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.SnapshotAnalysis(\n",
-        "                    monitoring_interval=Duration(seconds=86400),  # 1 day\n",
+        "            monitoring_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig(\n",
+        "                snapshot_analysis=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.SnapshotAnalysis(\n",
+        "                    monitoring_interval_days=1,  # 1 day\n",
         "                    staleness_days=30,\n",
         "                ),\n",
-        "                numerical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                numerical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
         "                    value=0.001,\n",
         "                ),\n",
-        "                categorical_threshold_config=v1beta1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
+        "                categorical_threshold_config=v1_featurestore_monitoring_pb2.FeaturestoreMonitoringConfig.ThresholdConfig(\n",
         "                    value=0.001,\n",
         "                ),\n",
         "            ),\n",
@@ -891,8 +919,8 @@
       "source": [
         "## Search created features\n",
         "\n",
-        "While the [ListFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.ListFeatures) method allows you to easily view all features of a single\n",
-        "entity type, the [SearchFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#google.cloud.aiplatform.v1beta1.FeaturestoreService.SearchFeatures) method searches across all featurestores\n",
+        "While the [ListFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.ListFeatures) method allows you to easily view all features of a single\n",
+        "entity type, the [SearchFeatures](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#google.cloud.aiplatform.v1.FeaturestoreService.SearchFeatures) method searches across all featurestores\n",
         "and entity types in a given location (such as `us-central1`). This can help you discover features that were created by someone else.\n",
         "\n",
         "You can query based on feature properties including feature ID, entity type ID,\n",
@@ -1206,7 +1234,7 @@
       },
       "source": [
         "The\n",
-        "[Online Serving APIs](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1beta1#featurestoreonlineservingservice)\n",
+        "[Online Serving APIs](https://cloud.google.com/vertex-ai/docs/reference/rpc/google.cloud.aiplatform.v1#featurestoreonlineservingservice)\n",
         "lets you serve feature values for small batches of entities. It's designed for latency-sensitive service, such as online model prediction. For example, for a movie service, you might want to quickly shows movies that the current user would most likely watch by using online predictions."
       ]
     },

notebooks/community/ml_ops/stage1/get_started_with_data_labeling.ipynb

@@ -212,7 +212,7 @@
         "\n",
         "3. [Enable the Vertex AI APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component)\n",
         "\n",
-        "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebooks.\n",
+        "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Vertex AI Workbench Notebooks.\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",
@@ -374,15 +374,8 @@
         "### Authenticate your Google Cloud account\n",
         "\n",
         "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
-        "authenticated. Skip this step."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "32e1cd21a5d5"
-      },
-      "source": [
+        "authenticated. \n",
+        "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
         "when prompted to authenticate your account via oAuth.\n",
         "\n",

notebooks/community/ml_ops/stage5/get_started_with_vertex_endpoints.ipynb

@@ -340,7 +340,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. \n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
         "\n",
@@ -376,12 +376,11 @@
         "import sys\n",
         "\n",
         "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
         "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
         "    \"DL_ANACONDA_HOME\"\n",
         "):\n",
         "    if \"google.colab\" in sys.modules:\n",
-        "        IS_COLAB = True\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
         "        google_auth.authenticate_user()\n",
@@ -428,8 +427,9 @@
       },
       "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"
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -785,7 +785,7 @@
       },
       "outputs": [],
       "source": [
-        "endpoint.gca_resource"
+        "print(endpoint.gca_resource)"
       ]
     },
     {
@@ -908,7 +908,7 @@
       },
       "outputs": [],
       "source": [
-        "endpoint.gca_resource.deployed_models[0]"
+        "print(endpoint.gca_resource.deployed_models[0])"
       ]
     },
     {
@@ -1203,12 +1203,10 @@
         "\n",
         "In this pipeline, you create an `Endpoint` resource, and then you deploy a `Model` resource to the `Endpoint` resource. The `Model` resource  to deploy is your existing TFHub model which you previously imported as a `Model` resource. The steps are:\n",
         "\n",
-        "- For pipeline parameters, pass the resource name and resource URI for the existing `Model` resource.\n",
-        "- Use the `importer_node()` component to create a `VertexModel` pipeline artifact for the model.\n",
+        "- For pipeline parameters, pass the resource name for the existing `Model` resource.\n",
+        "- Use the `GetVertexModelOp()` component to create a `VertexModel` pipeline artifact for the model.\n",
         "- Create an `Endpoint` resource.\n",
-        "- Using the `VertexModel` pipeline artifact, deploy the `Model` resource to the `Endpoint` resource.\n",
-        "\n",
-        "*Note:* This example currently blocked by internal issue: b/219835305"
+        "- Using the `VertexModel` pipeline artifact, deploy the `Model` resource to the `Endpoint` resource."
       ]
     },
     {
@@ -1225,20 +1223,6 @@
         "\n",
         "PIPELINE_ROOT = \"{}/pipeline_root/endpoint_example\".format(BUCKET_URI)\n",
         "\n",
-        "# (WORKAROUND  b/219835305)\n",
-        "@component(\n",
-        "    base_image=\"python:3.9\",\n",
-        "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
-        ")\n",
-        "def return_unmanaged_model(\n",
-        "    serving_image: str, artifact_uri: str, resource_name: str, model: Output[Artifact]\n",
-        "):\n",
-        "    model.metadata[\"containerSpec\"] = {\"imageUri\": serving_image}\n",
-        "\n",
-        "    model.metadata[\"resourceName\"] = resource_name\n",
-        "\n",
-        "    model.uri = artifact_uri\n",
-        "\n",
         "\n",
         "@dsl.pipeline(\n",
         "    name=\"create-endpoint-deploy-model\",\n",
@@ -1246,34 +1230,16 @@
         ")\n",
         "def pipeline(\n",
         "    display_name: str,\n",
-        "    resource_uri: str,\n",
         "    resource_name: str,\n",
-        "    # Model properties (WORKAROUND  b/219835305)\n",
-        "    serving_image: str,\n",
-        "    artifact_uri: str,\n",
         "    project: str = PROJECT_ID,\n",
         "    region: str = REGION,\n",
         "):\n",
-        "    from google_cloud_pipeline_components.types import artifact_types\n",
+        "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
+        "        GetVertexModelOp\n",
         "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
         "                                                              ModelDeployOp)\n",
-        "    from kfp.v2.components import importer_node\n",
         "\n",
-        "    # Desired sequence: blocked by b/219835305\n",
-        "    \"\"\"\n",
-        "    model = importer_node.importer(\n",
-        "        artifact_uri=resource_uri,\n",
-        "        artifact_class=artifact_types.VertexModel,\n",
-        "        metadata={\"resourceName\": resource_name},\n",
-        "    )\n",
-        "    \"\"\"\n",
-        "\n",
-        "    # (WORKAROUND  b/219835305)\n",
-        "    model = return_unmanaged_model(\n",
-        "        serving_image=serving_image,\n",
-        "        artifact_uri=artifact_uri,\n",
-        "        resource_name=resource_name,\n",
-        "    )\n",
+        "    model = GetVertexModelOp(model_resource_name=resource_name)\n",
         "\n",
         "    endpoint_op = EndpointCreateOp(\n",
         "        project=project,\n",
@@ -1281,7 +1247,7 @@
         "        display_name=display_name,\n",
         "    )\n",
         "\n",
-        "    deploy_op = ModelDeployOp(\n",
+        "    _ = ModelDeployOp(\n",
         "        model=model.outputs[\"model\"],\n",
         "        endpoint=endpoint_op.outputs[\"endpoint\"],\n",
         "        dedicated_resources_min_replica_count=1,\n",
@@ -1310,7 +1276,6 @@
         "\n",
         "- `display_name`: The display name for the generated Vertex AI resources.\n",
         "- `resource_name`: The resource name of the existing `Model` resource.\n",
-        "- `resource_uri`: The resource uri of the existing `Model` resource.\n",
         "- `project`: The project ID.\n",
         "- `region`: The region."
       ]
@@ -1323,10 +1288,6 @@
       },
       "outputs": [],
       "source": [
-        "# Model properties (WORKAROUND  b/219835305)\n",
-        "SERVING_CONTAINER_URI = model.gca_resource.container_spec.image_uri\n",
-        "ARTIFACT_URI = model.gca_resource.artifact_uri\n",
-        "\n",
         "try:\n",
         "    pipeline = aip.PipelineJob(\n",
         "        display_name=\"create-endpoint-deploy-pipeline\",\n",
@@ -1335,11 +1296,6 @@
         "        parameter_values={\n",
         "            \"display_name\": \"create_endpoint_and_deploy_model_\" + TIMESTAMP,\n",
         "            \"resource_name\": model.resource_name,\n",
-        "            \"resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
-        "            + model.resource_name,\n",
-        "            # Model properties (WORKAROUND  b/219835305)\n",
-        "            \"serving_image\": SERVING_CONTAINER_URI,\n",
-        "            \"artifact_uri\": ARTIFACT_URI,\n",
         "            \"project\": PROJECT_ID,\n",
         "            \"region\": REGION,\n",
         "        },\n",
@@ -1488,7 +1444,7 @@
         "\n",
         "- For pipeline parameters, pass the resource names and resource URIs for the existing `Model` and `Endpoint` resource.\n",
         "- Use the `importer_node()` component to create a `VertexModel` pipeline artifact for the model.\n",
-        "- Use the `importer_node()` component to create a `VertexEndpoint` pipeline artifact for the endpoint.\n",
+        "- Use the `GetVertexModelOp()` component to create a `VertexModel` pipeline artifact for the model.\n",
         "- Using the `VertexModel` and `VertexEndpoint` pipeline artifacts, deploy the `Model` resource to the `Endpoint` resource.\n",
         "\n",
         "*Note:* This example currently blocked by internal issue: b/219835305"
@@ -1504,6 +1460,7 @@
       "source": [
         "PIPELINE_ROOT = \"{}/pipeline_root/endpoint_example_2\".format(BUCKET_URI)\n",
         "\n",
+        "\n",
         "# (WORKAROUND  b/219835305)\n",
         "@component(\n",
         "    base_image=\"python:3.9\",\n",
@@ -1520,35 +1477,23 @@
         ")\n",
         "def pipeline(\n",
         "    display_name: str,\n",
-        "    model_resource_uri: str,\n",
         "    model_resource_name: str,\n",
         "    endpoint_resource_uri: str,\n",
         "    endpoint_resource_name: str,\n",
-        "    # Model properties (WORKAROUND  b/219835305)\n",
-        "    serving_image: str,\n",
-        "    artifact_uri: str,\n",
         "    project: str = PROJECT_ID,\n",
         "    region: str = REGION,\n",
         "):\n",
-        "    from google_cloud_pipeline_components.types import artifact_types\n",
+        "    from google_cloud_pipeline_components.experimental.evaluation import \\\n",
+        "        GetVertexModelOp\n",
         "    from google_cloud_pipeline_components.v1.endpoint import ModelDeployOp\n",
-        "    from kfp.v2.components import importer_node\n",
         "\n",
         "    # Desired sequence: blocked by b/219835305\n",
         "    \"\"\"\n",
-        "    model = importer_node.importer(\n",
-        "        artifact_uri=resource_uri,\n",
-        "        artifact_class=artifact_types.VertexModel,\n",
-        "        metadata={\"resourceName\": resource_name},\n",
-        "    )\n",
+        "    from kfp.v2.components import importer_node\n",
+        "    from google_cloud_pipeline_components.types import artifact_types\n",
         "    \"\"\"\n",
         "\n",
-        "    # (WORKAROUND  b/219835305)\n",
-        "    model = return_unmanaged_model(\n",
-        "        serving_image=serving_image,\n",
-        "        artifact_uri=artifact_uri,\n",
-        "        resource_name=model_resource_name,\n",
-        "    )\n",
+        "    model = GetVertexModelOp(model_resource_name=model_resource_name)\n",
         "\n",
         "    # Desired sequence: blocked by b/219835305\n",
         "    \"\"\"\n",
@@ -1562,7 +1507,7 @@
         "    # (WORKAROUND  b/219835305)\n",
         "    endpoint = return_unmanaged_endpoint(resource_name=endpoint_resource_name)\n",
         "\n",
-        "    deploy_op = ModelDeployOp(\n",
+        "    _ = ModelDeployOp(\n",
         "        model=model.outputs[\"model\"],\n",
         "        endpoint=endpoint.outputs[\"endpoint\"],\n",
         "        dedicated_resources_min_replica_count=1,\n",
@@ -1591,7 +1536,6 @@
         "\n",
         "- `display_name`: The display name for the generated Vertex AI resources.\n",
         "- `model_resource_name`: The resource name of the existing `Model` resource.\n",
-        "- `model_resource_uri`: The resource uri of the existing `Model` resource.\n",
         "- `endpoint_resource_name`: The resource name of the existing `Endpoint` resource.\n",
         "- `endpoint_resource_uri`: The resource uri of the existing `Endpoint` resource.\n",
         "- `project`: The project ID.\n",
@@ -1614,14 +1558,9 @@
         "        parameter_values={\n",
         "            \"display_name\": \"deploy_model_existing_endpoint_\" + TIMESTAMP,\n",
         "            \"model_resource_name\": model.resource_name,\n",
-        "            \"model_resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
-        "            + model.resource_name,\n",
         "            \"endpoint_resource_name\": endpoint.resource_name,\n",
         "            \"endpoint_resource_uri\": \"https://us-central1-aiplatform.googleapis.com/v1/\"\n",
         "            + endpoint.resource_name,\n",
-        "            # Model properties (WORKAROUND  b/219835305)\n",
-        "            \"serving_image\": SERVING_CONTAINER_URI,\n",
-        "            \"artifact_uri\": ARTIFACT_URI,\n",
         "            \"project\": PROJECT_ID,\n",
         "            \"region\": REGION,\n",
         "        },\n",

notebooks/community/ml_ops/stage5/get_started_with_vertex_private_endpoints.ipynb

@@ -385,12 +385,11 @@
         "import sys\n",
         "\n",
         "# If on Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = False\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
         "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
         "    \"DL_ANACONDA_HOME\"\n",
         "):\n",
         "    if \"google.colab\" in sys.modules:\n",
-        "        IS_COLAB = True\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
         "        google_auth.authenticate_user()\n",
@@ -1068,7 +1067,6 @@
         "- `model`: The `Model` resource.\n",
         "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
         "- `machine_type`: The machine type for each VM instance.\n",
-        "- `traffic_split`: Set to `{}` to indicate no traffic split.\n",
         "\n",
         "Do to the requirements to provision the resource, this may take upto a few minutes."
       ]
@@ -1085,7 +1083,6 @@
         "    model=model,\n",
         "    deployed_model_display_name=\"example_\" + TIMESTAMP,\n",
         "    machine_type=DEPLOY_COMPUTE,\n",
-        "    traffic_split={},  # no traffic split\n",
         ")\n",
         "\n",
         "print(endpoint)"
@@ -1187,62 +1184,6 @@
         "    f.write(json.dumps({\"instances\": [{serving_input: {\"b64\": b64str}}]}))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "23e995c35fd6"
-      },
-      "source": [
-        "#### Construct the `Private Endpoint` URI\n",
-        "\n",
-        "Next, you construct the URI for the `Private Endpoint`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "97b248b2efb5"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint_id = endpoint.resource_name\n",
-        "\n",
-        "ENDPOINT_URL = ! gcloud beta ai endpoints describe {endpoint_id} \\\n",
-        "  --region={REGION} \\\n",
-        "  --format=\"value(deployedModels.privateEndpoints.predictHttpUri)\"\n",
-        "\n",
-        "private_url = ENDPOINT_URL[1]\n",
-        "print(private_url)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "27605b5f0c3a"
-      },
-      "source": [
-        "### Make the prediction request using curl\n",
-        "\n",
-        "Use `curl` to make the prediction request to the private URI."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6cb568e6bb49"
-      },
-      "outputs": [],
-      "source": [
-        "output = ! curl -X POST -d@instances.json $private_url\n",
-        "\n",
-        "predictions = output[5]\n",
-        "print(predictions)\n",
-        "\n",
-        "! rm test.jpg instances.json"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1251,7 +1192,7 @@
       "source": [
         "### Make the prediction request using SDK\n",
         "\n",
-        "Finally, use the `Vertex AI SDK` to make a prediction request."
+        "Next, use the `Vertex AI SDK` to make a prediction request."
       ]
     },
     {

notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.ipynb

@@ -1,879 +0,0 @@
-{
-  "cells": [
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "copyright"
-      },
-      "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": "title:generic,gcp"
-      },
-      "source": [
-        "# E2E ML on GCP: MLOps stage 6 : serving: get started with re-importing AutoML tabular models\n",
-        "\n",
-        "<table align=\"left\">\n",
-        "  <td>\n",
-        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.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://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.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://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_automl_tabular_exported_deploy.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>\n",
-        "<br/><br/><br/>"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "overview:mlops"
-      },
-      "source": [
-        "## Overview\n",
-        "\n",
-        "\n",
-        "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with AutoML Training."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "objective:mlops,stage2,get_started_automl_training"
-      },
-      "source": [
-        "### Objective\n",
-        "\n",
-        "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.\n",
-        "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `AutoML Tabular`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Prediction`\n",
-        "\n",
-        "The steps performed include:\n",
-        "\n",
-        "- Importing a pretrained AutoML tabular exported model artifacts, as a `Model` resource.\n",
-        "- Create an `Endpoint` resource.\n",
-        "- Deploy the `Model` resource to the `Endpoint` resource.\n",
-        "- Make a prediction."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:flowers,icn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "This tutorial uses a pretrained AutoML tabular model with exported model artifacts.\n",
-        "\n",
-        "\n",
-        "The tabular dataset used for the pretrained model is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp).\n",
-        "\n",
-        "*Note:* This version of the exported model contains the custom op and requires the model server: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "fb3451ce8e47"
-      },
-      "source": [
-        "### Costs\n",
-        "This tutorial uses billable components of Google Cloud:\n",
-        "\n",
-        "- Vertex AI\n",
-        "- Cloud Storage\n",
-        "\n",
-        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "source": [
-        "## Installations\n",
-        "\n",
-        "Install the packages required for executing this notebook."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_mlops"
-      },
-      "outputs": [],
-      "source": [
-        "import os\n",
-        "\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",
-        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
-        "USER_FLAG = \"\"\n",
-        "if IS_WORKBENCH_NOTEBOOK:\n",
-        "    USER_FLAG = \"--user\"\n",
-        "\n",
-        "# Install the packages\n",
-        "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "! pip3 install --upgrade google-cloud-storage {USER_FLAG} -q"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "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."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "restart"
-      },
-      "outputs": [],
-      "source": [
-        "import os\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)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "project_id"
-      },
-      "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 $300 free credit towards your compute/storage costs.\n",
-        "\n",
-        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
-        "\n",
-        "1. [Enable the Vertex AI, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage_component).\n",
-        "\n",
-        "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
-        "\n",
-        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\n",
-        "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "56d591439df1"
-      },
-      "source": [
-        "#### Set your project ID\n",
-        "\n",
-        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "set_project_id"
-      },
-      "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"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "region"
-      },
-      "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)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "region"
-      },
-      "outputs": [],
-      "source": [
-        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
-        "\n",
-        "if REGION == \"[your-region]\":\n",
-        "    REGION = \"us-central1\""
-      ]
-    },
-    {
-      "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": {
-        "id": "3ffa6b6c7cdb"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
-        "\n",
-        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
-        "\n",
-        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\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",
-        "1. **Click Create service account**.\n",
-        "\n",
-        "2. In the **Service account name** field, enter a name, and click **Create**.\n",
-        "\n",
-        "3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
-        "\n",
-        "4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
-        "\n",
-        "5. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "2b72272258fc"
-      },
-      "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 Vertex AI Workbench, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\n",
-        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
-        "    \"DL_ANACONDA_HOME\"\n",
-        "):\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 ''"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "bucket:mbsdk"
-      },
-      "source": [
-        "### Create a Cloud Storage bucket\n",
-        "\n",
-        "**The following steps are required, regardless of your notebook environment.**\n",
-        "\n",
-        "When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
-        "\n",
-        "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "bucket"
-      },
-      "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_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
-        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "source": [
-        "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "create_bucket"
-      },
-      "outputs": [],
-      "source": [
-        "! 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"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "import google.cloud.aiplatform as aiplatform"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "source": [
-        "### Initialize Vertex AI SDK for Python\n",
-        "\n",
-        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "init_aip:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "machine:training"
-      },
-      "source": [
-        "#### Set machine type\n",
-        "\n",
-        "Next, set the machine type to use for training.\n",
-        "\n",
-        "- Set the variable `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training.\n",
-        " - `machine type`\n",
-        "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
-        "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
-        "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
-        " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
-        "\n",
-        "*Note: The following is not supported for training:*\n",
-        "\n",
-        " - `standard`: 2 vCPUs\n",
-        " - `highcpu`: 2, 4 and 8 vCPUs\n",
-        "\n",
-        "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "machine:training"
-      },
-      "outputs": [],
-      "source": [
-        "if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n",
-        "    MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n",
-        "else:\n",
-        "    MACHINE_TYPE = \"n1-standard\"\n",
-        "\n",
-        "VCPU = \"4\"\n",
-        "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
-        "print(\"Train machine type\", DEPLOY_COMPUTE)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "import_file:u_dataset,csv"
-      },
-      "source": [
-        "### Location of pretrained `AutoML Tabular` exported model\n",
-        "\n",
-        "Now set the variable `MODEL_PACKAGE` to the location of the exported model artifacts in Cloud Storage."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_file:flowers,csv,icn"
-      },
-      "outputs": [],
-      "source": [
-        "MODEL_PACKAGE = \"gs://cloud-samples-data/vertex-ai/tabular-workflows/models/custom_op\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "quick_peek:csv"
-      },
-      "source": [
-        "#### Quick peek at your model package.\n",
-        "\n",
-        "Next, take a look at the contents of the model package for the exported AutoML tabular model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "quick_peek:csv"
-      },
-      "outputs": [],
-      "source": [
-        "! gsutil ls {MODEL_PACKAGE}"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "automl_tabular_intro"
-      },
-      "source": [
-        "## AutoML tabular models\n",
-        "\n",
-        "AutoML can train the following types of tabular models:\n",
-        "\n",
-        "- classification\n",
-        "- regression\n",
-        "- forecasting\n",
-        "\n",
-        "A model can be trained for either automatic deployment to the cloud or exported for manual deployment to the cloud. In this tutorial, you use a pretrained exported AutoML tabular model.\n",
-        "\n",
-        "Learn more about [AutoML Model Types](https://cloud.google.com/vertex-ai/docs/start/automl-model-types)\n",
-        "\n",
-        "Learn more about [Exporting AutoML Tabular models](https://cloud.google.com/vertex-ai/docs/export/export-model-tabular)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c10efb34321b"
-      },
-      "source": [
-        "### Set the model server\n",
-        "\n",
-        "Next, you set the pre-built container for the model server. The container will be a version of `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server`. If the model package contains an `environment.json` file, use the container version specified by the key `container_uri`; otherwise, use `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:latest` "
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "a5f271de9040"
-      },
-      "outputs": [],
-      "source": [
-        "import json\n",
-        "\n",
-        "output = !gsutil cat {MODEL_PACKAGE}/environment.json\n",
-        "\n",
-        "MODEL_SERVER = json.loads(output[0])[\"container_uri\"]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "e8ce91147c93"
-      },
-      "source": [
-        "### Upload the pretrained exported `AutoML Tabular` model package to a `Vertex AI Model` resource\n",
-        "\n",
-        "Next, you upload the model artifacts for the pretrained exported `AutoML Tabular` model into a `Vertex AI Model` resource, using the `Model.upload()` method with the following parameters:\n",
-        "\n",
-        "- `display_name`: A human readable name for the `Model` resource.\n",
-        "- `artifact_uri`: The Cloud Storage location of the model package.\n",
-        "- `serving_container_image_uri`: The serving container image.\n",
-        "- `serving_container_ports`: The serving port.\n",
-        "\n",
-        "*Note:* When you upload the model artifacts to a `Vertex Model` resource, you specify the corresponding deployment container image."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7988eae27f80"
-      },
-      "outputs": [],
-      "source": [
-        "model = aiplatform.Model.upload(\n",
-        "    display_name=\"gsod_\" + TIMESTAMP,\n",
-        "    artifact_uri=MODEL_PACKAGE,\n",
-        "    serving_container_image_uri=MODEL_SERVER,\n",
-        "    serving_container_ports=[8080],\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "628de0914ba1"
-      },
-      "source": [
-        "## Creating an `Endpoint` resource\n",
-        "\n",
-        "You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
-        "\n",
-        "In this example, the following parameters are specified:\n",
-        "\n",
-        "- `display_name`: A human readable name for the `Endpoint` resource.\n",
-        "- `project`: Your project ID.\n",
-        "- `location`: Your region.\n",
-        "- `labels`: (optional) User defined metadata for the `Endpoint` in the form of key/value pairs.\n",
-        "\n",
-        "This method returns an `Endpoint` object.\n",
-        "\n",
-        "Learn more about [Vertex AI Endpoints](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0ea443f9593b"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint = aiplatform.Endpoint.create(\n",
-        "    display_name=\"gsod_\" + TIMESTAMP,\n",
-        "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
-        "    labels={\"your_key\": \"your_value\"},\n",
-        ")\n",
-        "\n",
-        "print(endpoint)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ca3fa3f6a894"
-      },
-      "source": [
-        "## Deploying `Model` resources to an `Endpoint` resource.\n",
-        "\n",
-        "You can deploy one of more `Vertex AI Model` resource instances to the same endpoint. Each `Vertex AI Model` resource that is deployed will have its own deployment container for the serving binary. \n",
-        "\n",
-        "*Note:* For this example, you specified the deployment container for the exported AutoML Tabular model in the previous step of uploading the model artifacts to a `Vertex AI Model` resource.\n",
-        "\n",
-        "To deploy, you specify the following additional configuration settings:\n",
-        "\n",
-        "- The machine type.\n",
-        "- The (if any) type and number of GPUs.\n",
-        "- Static, manual or auto-scaling of VM instances.\n",
-        "\n",
-        "In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
-        "\n",
-        "- `model`: The `Model` resource.\n",
-        "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
-        "- `machine_type`: The machine type for each VM instance.\n",
-        "\n",
-        "Do to the requirements to provision the resource, this may take upto a few minutes."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "4e93b034a72f"
-      },
-      "outputs": [],
-      "source": [
-        "response = endpoint.deploy(\n",
-        "    model=model,\n",
-        "    deployed_model_display_name=\"gsod_\" + TIMESTAMP,\n",
-        "    machine_type=DEPLOY_COMPUTE,\n",
-        ")\n",
-        "\n",
-        "print(response)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "f75331c946d5"
-      },
-      "source": [
-        "## Make a prediction\n",
-        "\n",
-        "Finally, you make an online prediction using the `endpoint()` method, with the following parameters:\n",
-        "\n",
-        "- `instances`: The instances to predict.\n",
-        "\n",
-        "The following is the for a prediction request:\n",
-        "\n",
-        "    [ INSTANCE_1, INSTANCE_2, ... ]\n",
-        "    \n",
-        "    INSTANCE : { \"column_1\": value, \"column_2\": value, ... }\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "72683bd9d777"
-      },
-      "outputs": [],
-      "source": [
-        "INSTANCES = [{\"year\": \"2020\", \"month\": \"1\", \"day\": \"23\"}]\n",
-        "\n",
-        "prediction = endpoint.predict(instances=INSTANCES)\n",
-        "print(prediction)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "endpoint_delete:mbsdk"
-      },
-      "source": [
-        "#### Delete the endpoint\n",
-        "\n",
-        "The method 'delete()' will delete the endpoint."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "endpoint_delete:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint.undeploy_all()\n",
-        "endpoint.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "model_delete:mbsdk"
-      },
-      "source": [
-        "#### Delete the model\n",
-        "\n",
-        "The method 'delete()' will delete the model."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "model_delete:mbsdk"
-      },
-      "outputs": [],
-      "source": [
-        "model.delete()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cleanup"
-      },
-      "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.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "cleanup"
-      },
-      "outputs": [],
-      "source": [
-        "# Set this to true only if you'd like to delete your bucket\n",
-        "delete_bucket = False\n",
-        "\n",
-        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil rm -r $BUCKET_URI"
-      ]
-    }
-  ],
-  "metadata": {
-    "colab": {
-      "name": "get_started_automl_tabular_exported_deploy.ipynb",
-      "toc_visible": true
-    },
-    "kernelspec": {
-      "display_name": "Python 3",
-      "name": "python3"
-    }
-  },
-  "nbformat": 4,
-  "nbformat_minor": 0
-}

notebooks/community/ml_ops/stage6/get_started_with_automl_tabular_exported_deploy.ipynb

@@ -0,0 +1,898 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "copyright"
+   },
+   "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": "title:generic,gcp"
+   },
+   "source": [
+    "# E2E ML on GCP: MLOps stage 6 : serving: get started with re-importing AutoML tabular models\n",
+    "\n",
+    "<table align=\"left\">\n",
+    "  <td>\n",
+    "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_with_automl_tabular_exported_deploy.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://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/ml_ops/stage6/get_started_automl_with_tabular_exported_deploy.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://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/ml_ops/stage6/get_started_automl_with_tabular_exported_deploy.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>\n",
+    "<br/><br/><br/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "overview:mlops"
+   },
+   "source": [
+    "## Overview\n",
+    "\n",
+    "\n",
+    "This tutorial demonstrates how to use Vertex AI for E2E MLOps on Google Cloud in production. This tutorial covers stage 2 : experimentation: get started with AutoML Training."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "objective:mlops,stage2,get_started_automl_training"
+   },
+   "source": [
+    "### Objective\n",
+    "\n",
+    "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.\n",
+    "\n",
+    "This tutorial uses the following Google Cloud ML services:\n",
+    "\n",
+    "- `AutoML Tabular`\n",
+    "- `Vertex AI Model` resource\n",
+    "- `Vertex AI Prediction`\n",
+    "\n",
+    "The steps performed include:\n",
+    "\n",
+    "- Importing a pretrained AutoML tabular exported model artifacts, as a `Model` resource.\n",
+    "- Create an `Endpoint` resource.\n",
+    "- Deploy the `Model` resource to the `Endpoint` resource.\n",
+    "- Make a prediction."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dataset:flowers,icn"
+   },
+   "source": [
+    "### Dataset\n",
+    "\n",
+    "This tutorial uses a pretrained AutoML tabular model with exported model artifacts.\n",
+    "\n",
+    "\n",
+    "The tabular dataset used for the pretrained model is the GSOD dataset from [BigQuery public datasets](https://cloud.google.com/bigquery/public-data). The version of the dataset you use only the fields year, month and day to predict the value of mean daily temperature (mean_temp).\n",
+    "\n",
+    "*Note:* This version of the exported model contains the custom op and requires the model server: us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "fb3451ce8e47"
+   },
+   "source": [
+    "### Costs\n",
+    "This tutorial uses billable components of Google Cloud:\n",
+    "\n",
+    "- Vertex AI\n",
+    "- Cloud Storage\n",
+    "\n",
+    "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage pricing](https://cloud.google.com/storage/pricing) and use the [Pricing Calculator](https://cloud.google.com/products/calculator/) to generate a cost estimate based on your projected usage."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "install_mlops"
+   },
+   "source": [
+    "## Installations\n",
+    "\n",
+    "Install the packages required for executing this notebook."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "install_mlops"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\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",
+    "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
+    "USER_FLAG = \"\"\n",
+    "if IS_WORKBENCH_NOTEBOOK:\n",
+    "    USER_FLAG = \"--user\"\n",
+    "\n",
+    "# Install the packages\n",
+    "\n",
+    "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
+    "! pip3 install --upgrade google-cloud-storage {USER_FLAG} -q"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "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."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "restart"
+   },
+   "outputs": [],
+   "source": [
+    "import os\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)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "project_id"
+   },
+   "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 $300 free credit towards your compute/storage costs.\n",
+    "\n",
+    "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+    "\n",
+    "1. [Enable the Vertex AI, Compute Engine and Cloud Storage APIs](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,compute_component,storage_component).\n",
+    "\n",
+    "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
+    "\n",
+    "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
+    "Cloud SDK uses the right project for all the commands in this notebook.\n",
+    "\n",
+    "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "56d591439df1"
+   },
+   "source": [
+    "#### Set your project ID\n",
+    "\n",
+    "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "set_project_id"
+   },
+   "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"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "region"
+   },
+   "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)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "region"
+   },
+   "outputs": [],
+   "source": [
+    "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+    "\n",
+    "if REGION == \"[your-region]\":\n",
+    "    REGION = \"us-central1\""
+   ]
+  },
+  {
+   "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": {
+    "id": "3ffa6b6c7cdb"
+   },
+   "source": [
+    "### Authenticate your Google Cloud account\n",
+    "\n",
+    "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
+    "\n",
+    "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\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",
+    "1. **Click Create service account**.\n",
+    "\n",
+    "2. In the **Service account name** field, enter a name, and click **Create**.\n",
+    "\n",
+    "3. In the **Grant this service account access to project** section, click the Role drop-down list. Type \"Vertex AI\" into the filter box, and select **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
+    "\n",
+    "4. Click Create. A JSON file that contains your key downloads to your local environment.\n",
+    "\n",
+    "5. Enter the path to your service account key as the GOOGLE_APPLICATION_CREDENTIALS variable in the cell below and run the cell."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "2b72272258fc"
+   },
+   "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 Vertex AI Workbench, then don't execute this code\n",
+    "IS_COLAB = \"google.colab\" in sys.modules\n",
+    "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+    "    \"DL_ANACONDA_HOME\"\n",
+    "):\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 ''"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "bucket:mbsdk"
+   },
+   "source": [
+    "### Create a Cloud Storage bucket\n",
+    "\n",
+    "**The following steps are required, regardless of your notebook environment.**\n",
+    "\n",
+    "When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
+    "\n",
+    "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "bucket"
+   },
+   "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_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+    "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "create_bucket"
+   },
+   "source": [
+    "**Only if your bucket doesn't already exist**: Run the following cell to create your Cloud Storage bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "create_bucket"
+   },
+   "outputs": [],
+   "source": [
+    "! 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"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "import_aip:mbsdk"
+   },
+   "outputs": [],
+   "source": [
+    "import google.cloud.aiplatform as aiplatform"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "init_aip:mbsdk"
+   },
+   "source": [
+    "### Initialize Vertex AI SDK for Python\n",
+    "\n",
+    "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "init_aip:mbsdk"
+   },
+   "outputs": [],
+   "source": [
+    "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "machine:training"
+   },
+   "source": [
+    "#### Set machine type\n",
+    "\n",
+    "Next, set the machine type to use for training.\n",
+    "\n",
+    "- Set the variable `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training.\n",
+    " - `machine type`\n",
+    "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
+    "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
+    "     - `n1-highcpu`: 0.9 GB of memory per vCPU\n",
+    " - `vCPUs`: number of \\[2, 4, 8, 16, 32, 64, 96 \\]\n",
+    "\n",
+    "*Note: The following is not supported for training:*\n",
+    "\n",
+    " - `standard`: 2 vCPUs\n",
+    " - `highcpu`: 2, 4 and 8 vCPUs\n",
+    "\n",
+    "*Note: You may also use n2 and e2 machine types for training and deployment, but they do not support GPUs*."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "machine:training"
+   },
+   "outputs": [],
+   "source": [
+    "if os.getenv(\"IS_TESTING_DEPLOY_MACHINE\"):\n",
+    "    MACHINE_TYPE = os.getenv(\"IS_TESTING_DEPLOY_MACHINE\")\n",
+    "else:\n",
+    "    MACHINE_TYPE = \"n1-standard\"\n",
+    "\n",
+    "VCPU = \"4\"\n",
+    "DEPLOY_COMPUTE = MACHINE_TYPE + \"-\" + VCPU\n",
+    "print(\"Train machine type\", DEPLOY_COMPUTE)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "import_file:u_dataset,csv"
+   },
+   "source": [
+    "### Location of pretrained `AutoML Tabular` exported model\n",
+    "\n",
+    "Now set the variable `MODEL_PACKAGE` to the location of the exported model artifacts in Cloud Storage."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "import_file:flowers,csv,icn"
+   },
+   "outputs": [],
+   "source": [
+    "MODEL_PACKAGE = \"gs://cloud-samples-data/vertex-ai/tabular-workflows/models/custom_op\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "quick_peek:csv"
+   },
+   "source": [
+    "#### Quick peek at your model package.\n",
+    "\n",
+    "Next, take a look at the contents of the model package for the exported AutoML tabular model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "quick_peek:csv"
+   },
+   "outputs": [],
+   "source": [
+    "! gsutil ls {MODEL_PACKAGE}"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "automl_tabular_intro"
+   },
+   "source": [
+    "## AutoML tabular models\n",
+    "\n",
+    "AutoML can train the following types of tabular models:\n",
+    "\n",
+    "- classification\n",
+    "- regression\n",
+    "- forecasting\n",
+    "\n",
+    "A model can be trained for either automatic deployment to the cloud or exported for manual deployment to the cloud. In this tutorial, you use a pretrained exported AutoML tabular model.\n",
+    "\n",
+    "Learn more about [AutoML Model Types](https://cloud.google.com/vertex-ai/docs/start/automl-model-types)\n",
+    "\n",
+    "Learn more about [Exporting AutoML Tabular models](https://cloud.google.com/vertex-ai/docs/export/export-model-tabular)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "c10efb34321b"
+   },
+   "source": [
+    "### Set the model server\n",
+    "\n",
+    "Next, you set the pre-built container for the model server. The container will be a version of `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server`. If the model package contains an `environment.json` file, use the container version specified by the key `container_uri`; otherwise, use `us-docker.pkg.dev/vertex-ai/automl-tabular/prediction-server:latest` "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "a5f271de9040"
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "output = !gsutil cat {MODEL_PACKAGE}/environment.json\n",
+    "\n",
+    "MODEL_SERVER = json.loads(output[0])[\"container_uri\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "e8ce91147c93"
+   },
+   "source": [
+    "### Upload the pretrained exported `AutoML Tabular` model package to a `Vertex AI Model` resource\n",
+    "\n",
+    "Next, you upload the model artifacts for the pretrained exported `AutoML Tabular` model into a `Vertex AI Model` resource, using the `Model.upload()` method with the following parameters:\n",
+    "\n",
+    "- `display_name`: A human readable name for the `Model` resource.\n",
+    "- `artifact_uri`: The Cloud Storage location of the model package.\n",
+    "- `serving_container_image_uri`: The serving container image.\n",
+    "- `serving_container_ports`: The serving port.\n",
+    "\n",
+    "*Note:* When you upload the model artifacts to a `Vertex Model` resource, you specify the corresponding deployment container image."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "7988eae27f80"
+   },
+   "outputs": [],
+   "source": [
+    "model = aiplatform.Model.upload(\n",
+    "    display_name=\"gsod_\" + TIMESTAMP,\n",
+    "    artifact_uri=MODEL_PACKAGE,\n",
+    "    serving_container_image_uri=MODEL_SERVER,\n",
+    "    serving_container_ports=[8080],\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "628de0914ba1"
+   },
+   "source": [
+    "## Creating an `Endpoint` resource\n",
+    "\n",
+    "You create an `Endpoint` resource using the `Endpoint.create()` method. At a minimum, you specify the display name for the endpoint. Optionally, you can specify the project and location (region); otherwise the settings are inherited by the values you set when you initialized the Vertex AI SDK with the `init()` method.\n",
+    "\n",
+    "In this example, the following parameters are specified:\n",
+    "\n",
+    "- `display_name`: A human readable name for the `Endpoint` resource.\n",
+    "- `project`: Your project ID.\n",
+    "- `location`: Your region.\n",
+    "- `labels`: (optional) User defined metadata for the `Endpoint` in the form of key/value pairs.\n",
+    "\n",
+    "This method returns an `Endpoint` object.\n",
+    "\n",
+    "Learn more about [Vertex AI Endpoints](https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "0ea443f9593b"
+   },
+   "outputs": [],
+   "source": [
+    "endpoint = aiplatform.Endpoint.create(\n",
+    "    display_name=\"gsod_\" + TIMESTAMP,\n",
+    "    project=PROJECT_ID,\n",
+    "    location=REGION,\n",
+    "    labels={\"your_key\": \"your_value\"},\n",
+    ")\n",
+    "\n",
+    "print(endpoint)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ca3fa3f6a894"
+   },
+   "source": [
+    "## Deploying `Model` resources to an `Endpoint` resource.\n",
+    "\n",
+    "You can deploy one of more `Vertex AI Model` resource instances to the same endpoint. Each `Vertex AI Model` resource that is deployed will have its own deployment container for the serving binary. \n",
+    "\n",
+    "*Note:* For this example, you specified the deployment container for the exported AutoML Tabular model in the previous step of uploading the model artifacts to a `Vertex AI Model` resource.\n",
+    "\n",
+    "To deploy, you specify the following additional configuration settings:\n",
+    "\n",
+    "- The machine type.\n",
+    "- The (if any) type and number of GPUs.\n",
+    "- Static, manual or auto-scaling of VM instances.\n",
+    "\n",
+    "In this example, you deploy the model with the minimal amount of specified parameters, as follows:\n",
+    "\n",
+    "- `model`: The `Model` resource.\n",
+    "- `deployed_model_displayed_name`: The human readable name for the deployed model instance.\n",
+    "- `machine_type`: The machine type for each VM instance.\n",
+    "\n",
+    "Do to the requirements to provision the resource, this may take upto a few minutes."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4e93b034a72f"
+   },
+   "outputs": [],
+   "source": [
+    "response = endpoint.deploy(\n",
+    "    model=model,\n",
+    "    deployed_model_display_name=\"gsod_\" + TIMESTAMP,\n",
+    "    machine_type=DEPLOY_COMPUTE,\n",
+    ")\n",
+    "\n",
+    "print(response)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "f75331c946d5"
+   },
+   "source": [
+    "## Make a prediction\n",
+    "\n",
+    "Finally, you make an online prediction using the `endpoint()` method, with the following parameters:\n",
+    "\n",
+    "- `instances`: The instances to predict.\n",
+    "\n",
+    "The following is the for a prediction request:\n",
+    "\n",
+    "    [ INSTANCE_1, INSTANCE_2, ... ]\n",
+    "    \n",
+    "    INSTANCE : { \"column_1\": value, \"column_2\": value, ... }\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "72683bd9d777"
+   },
+   "outputs": [],
+   "source": [
+    "INSTANCES = [{\"year\": \"2020\", \"month\": \"1\", \"day\": \"23\"}]\n",
+    "\n",
+    "prediction = endpoint.predict(instances=INSTANCES)\n",
+    "print(prediction)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "endpoint_delete:mbsdk"
+   },
+   "source": [
+    "#### Delete the endpoint\n",
+    "\n",
+    "The method 'delete()' will delete the endpoint."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "endpoint_delete:mbsdk"
+   },
+   "outputs": [],
+   "source": [
+    "endpoint.undeploy_all()\n",
+    "endpoint.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "model_delete:mbsdk"
+   },
+   "source": [
+    "#### Delete the model\n",
+    "\n",
+    "The method 'delete()' will delete the model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "model_delete:mbsdk"
+   },
+   "outputs": [],
+   "source": [
+    "model.delete()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "cleanup"
+   },
+   "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.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "cleanup"
+   },
+   "outputs": [],
+   "source": [
+    "# Set this to true only if you'd like to delete your bucket\n",
+    "delete_bucket = False\n",
+    "\n",
+    "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+    "    ! gsutil rm -r $BUCKET_URI"
+   ]
+  }
+ ],
+ "metadata": {
+  "colab": {
+   "name": "get_started_automl_tabular_exported_deploy.ipynb",
+   "toc_visible": true
+  },
+  "environment": {
+   "kernel": "python3",
+   "name": "common-cpu.m95",
+   "type": "gcloud",
+   "uri": "gcr.io/deeplearning-platform-release/base-cpu:m95"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

notebooks/community/ml_ops/stage6/get_started_with_tf_serving.ipynb

@@ -702,7 +702,7 @@
         "        + f\"/{PRIVATE_REPO}\"\n",
         "        + \"/tf_serving:gpu\"\n",
         "    )\n",
-        "    TF_IMAGE = \"tensorflow/serving:latest-gpu\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:2.5.4-gpu\"\n",
         "else:\n",
         "    DEPLOY_IMAGE = (\n",
         "        f\"{REGION}-docker.pkg.dev/\"\n",
@@ -710,15 +710,15 @@
         "        + f\"/{PRIVATE_REPO}\"\n",
         "        + \"/tf_serving:cpu\"\n",
         "    )\n",
-        "    TF_IMAGE = \"tensorflow/serving:latest\"\n",
+        "    TF_IMAGE = \"tensorflow/serving:2.5.4\"\n",
         "\n",
         "if not IS_COLAB:\n",
         "    if DEPLOY_GPU:\n",
-        "        ! sudo docker pull tensorflow/serving:latest-gpu\n",
+        "        ! sudo docker pull tensorflow/serving:2.5.4-gpu\n",
         "    else:\n",
-        "        ! sudo docker pull tensorflow/serving:latest\n",
+        "        ! sudo docker pull tensorflow/serving:2.5.4\n",
         "\n",
-        "    ! docker tag tensorflow/serving $DEPLOY_IMAGE\n",
+        "    ! docker tag $TF_IMAGE $DEPLOY_IMAGE\n",
         "    ! docker push $DEPLOY_IMAGE\n",
         "else:\n",
         "    # install docker daemon\n",
@@ -1214,6 +1214,20 @@
         "     [1.0,3.0,\"cat1\"],\n",
         "     [2.0,4.0,\"cat2\"]\n",
         "    ]}\n",
+        "    \n",
+        "**BigQuery**\n",
+        "\n",
+        "Each row is converted to a JSON array. For example:\n",
+        "\n",
+        "    [1.0,3.0,\"cat1\"]\n",
+        "    [2.0,4.0,\"cat2\"]\n",
+        "    \n",
+        "The batch server generates the pivot data with the same format. The generated pivot data is then wrapped into a payload request:\n",
+        "\n",
+        "    {\"instances\": [\n",
+        "     [1.0,3.0,\"cat1\"],\n",
+        "     [2.0,4.0,\"cat2\"]\n",
+        "    ]}\n",
         "\n",
         "**TFRecords**\n",
         "\n",
@@ -1420,7 +1434,7 @@
       },
       "outputs": [],
       "source": [
-        "delete_bucket = False\n",
+        "delete_bucket = True\n",
         "delete_model = True\n",
         "delete_endpoint = True\n",
         "delete_batch_job = True\n",

notebooks/community/prediction/custom_prediction_routines/SDK_Custom_Preprocess.ipynb

@@ -673,7 +673,7 @@
         "Before you can deploy your model for serving, Vertex AI needs access to the following files in Cloud Storage:\n",
         "\n",
         "* `model.joblib` (model artifact)\n",
-        "* `preprocessor.pkl` (model artifact)\n",
+        "* `preprocessor.pkl` (preprocessor code)\n",
         "\n",
         "Run the following commands to upload your files:"
       ]

notebooks/notebook_template.ipynb

@@ -29,6 +29,8 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
+        "# [TODO] Add your H1 title heading here\n",
+        "\n",
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
@@ -80,7 +82,7 @@
         "\n",
         "The steps performed include:\n",
         "\n",
-        "- * {TODO: Add high level bullets for the steps of performed in the notebook}"
+        "- *{TODO: Add high level bullets for the steps of performed in the notebook}*"
       ]
     },
     {
@@ -109,35 +111,31 @@
         "This tutorial uses billable components of Google Cloud:\n",
         "\n",
         "* Vertex AI\n",
+        "* {TODO: BigQyuery}\n",
         "* Cloud Storage\n",
         "\n",
-        "{TODO: Include links to pricing documentation for each product you listed above.}\n",
+        "{TODO: Include links to pricing documentation for each product you listed above.\n",
+        " NOTE: If you use BigQuery or Dataflow, you need to add this to the pricing.\n",
+        "}\n",
         "\n",
-        "Learn about [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing) and [Cloud Storage\n",
-        "pricing](https://cloud.google.com/storage/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
+        "{ TODO: [BigQuery pricing](https://cloud.google.com/bigquery/pricing), }\n",
+        "and [Cloud Storage pricing](https://cloud.google.com/storage/pricing), \n",
+        "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
         "to generate a cost estimate based on your projected usage."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ze4-nDLfK4pw"
-      },
-      "source": [
-        "### Set up your local development environment\n",
-        "\n",
-        "**If you are using Colab or Vertex AI Workbench Notebooks**, your environment already meets\n",
-        "all the requirements to run this notebook. You can skip this step."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "gCuSR8GkAgzl"
       },
       "source": [
+        "### Set up your local development environment\n",
+        "\n",
+        "**If you are using Colab or Vertex AI Workbench Notebooks**, your environment already meets\n",
+        "all the requirements to run this notebook. You can skip this step.\n",
+        "\n",
         "**Otherwise**, make sure your environment meets this notebook's requirements.\n",
         "You need the following:\n",
         "\n",
@@ -204,7 +202,7 @@
         "    USER_FLAG = \"--user\"\n",
         "\n",
         "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "# TODO: Add remaining package installs here"
+        "# TODO: Add remaining package installs here. All packages should be on a single pip install to resolve dependencies"
       ]
     },
     {
@@ -237,21 +235,14 @@
         "    app.kernel.do_shutdown(True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lWEdiXsJg0XY"
-      },
-      "source": [
-        "## Before you begin"
-      ]
-    },
     {
       "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",
@@ -412,21 +403,14 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "dr--iN2kAylZ"
+        "id": "sBCra4QMA2wR"
       },
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
         "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
-        "authenticated. Skip this step."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sBCra4QMA2wR"
-      },
-      "source": [
+        "authenticated. \n",
+        "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
         "when prompted to authenticate your account via oAuth.\n",
         "\n",
@@ -481,7 +465,7 @@
         "    # 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 ''"
+        "        %env GOOGLE_APPLICATION_CREDENTIALS '[your-service-account-key-path]'"
       ]
     },
     {
@@ -497,7 +481,7 @@
         "\n",
         "{TODO: Adjust wording in the first paragraph to fit your use case - explain how your tutorial uses the Cloud Storage bucket. The example below shows how Vertex AI uses the bucket for training.}\n",
         "\n",
-        "When you submit a training job using the Cloud SDK, you upload a Python package\n",
+        "When you submit a training job using the Vertex AI SDK, you upload a Python package\n",
         "containing your training code to a Cloud Storage bucket. Vertex AI runs\n",
         "the code from this package. In this tutorial, Vertex AI also saves the\n",
         "trained model that results from your job in the same bucket. Using this model artifact, you can then\n",

notebooks/notebook_template_review.py

@@ -56,7 +56,7 @@ def parse_notebook(path):
         # cell 1 is copyright
         nth = 0
         cell, nth = get_cell(path, cells, nth)
-        if not cell['source'][0].startswith('# Copyright'):
+        if not 'Copyright' in cell['source'][0]:
             report_error(path, 0, "missing copyright cell")
             
         # check for notices

notebooks/official/CODEOWNERS

@@ -10,12 +10,13 @@
 /tabnet/tabnet_vertex_tutorial.ipynb @longtle
 
 /migration @andrewferlitsch
-/explainabl_ai   
+/explainabl_ai
 /pipelines @andrewferlitsch
 /ml_metadata @andrewferlitsch
 /model_monitoring @andrewferlitsch
 /tensorboard @zbl94
 
+/bigquery_ml/bqml-online-prediction.ipynb @polong-lin
 /model_monitoring/model_monitoring.ipynb @mco-gh
 /ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb @jialuzh
 /ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb @jialuzh
@@ -27,5 +28,10 @@
 /pipelines/google_cloud_pipelines_dataproc_tabular @inardini
 /automl/automl_forecasting_bqml_arima_plus_comparison.ipynb @TheMichaelHu
 /automl/automl_tabular_on_vertex_pipelines.ipynb @helinwang
-/custom/custom_training_tensorboard_profiler.ipynb @gericdong
+/custom/custom_training_tensorboard_profiler.ipynb @itseric
 /workbench/spark/spark_sample_notebook.ipynb @bradmiro
+/workbench/spark/spark_ml.ipynb @bradmiro
+/model-registry/bqml-vertexai-model-registry.ipynb @soheilazangeneh
+/workbench/exploratory_data_analysis/explore_data_in_bigquery_with_workbench.ipynb @alokpattani
+/model_evaluation/automl_tabular_classification_model_evaluation.ipynb @soheilazangeneh
+/model_evaluation/automl_tabular_regression_model_evaluation.ipynb @soheilazangeneh

notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb

@@ -176,7 +176,10 @@
         "else:\n",
         "    USER_FLAG = \"\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-bigquery[pandas] google-cloud-aiplatform google-cloud-pipeline-components $USER_FLAG"
+        "! (pip3 install --upgrade $USER_FLAG \\\n",
+        "    google-cloud-bigquery[pandas]==2.34.4 \\\n",
+        "    google-cloud-aiplatform==1.16.1 \\\n",
+        "    google-cloud-pipeline-components==1.0.18)"
       ]
     },
     {

notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb

@@ -855,6 +855,7 @@
         "    run_distillation=run_distillation,\n",
         "    dataflow_subnetwork=dataflow_subnetwork,\n",
         "    dataflow_use_public_ips=dataflow_use_public_ips,\n",
+        "    export_additional_model_without_custom_ops=export_additional_model_without_custom_ops,\n",
         ")\n",
         "\n",
         "job_id = \"automl-tabular-{}\".format(uuid.uuid4())\n",

notebooks/official/automl/sdk_automl_image_object_detection_batch.ipynb

@@ -8,7 +8,7 @@
       },
       "outputs": [],
       "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# Copyright 2021 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",
@@ -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": {
@@ -101,6 +90,17 @@
         "* 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": {
@@ -201,7 +201,7 @@
       },
       "outputs": [],
       "source": [
-        "! pip3 install -U google-cloud-storage $USER_FLAG"
+        "! pip3 install -U --upgrade tensorflow google-cloud-storage $USER_FLAG"
       ]
     },
     {
@@ -213,17 +213,6 @@
         "Install the latest version of *tensorflow* library."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_tensorflow"
-      },
-      "outputs": [],
-      "source": [
-        "! pip3 install --upgrade tensorflow $USER_FLAG"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -383,9 +372,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -396,9 +385,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -409,7 +405,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated.\n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
         "\n",
@@ -494,7 +490,7 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "    BUCKET_URI = \"gs://\" + PROJECT_ID + \"aip-\" + UUID"
       ]
     },
     {
@@ -669,7 +665,7 @@
       "outputs": [],
       "source": [
         "dataset = aiplatform.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"Salads\" + \"_\" + UUID,\n",
         "    gcs_source=[IMPORT_FILE],\n",
         "    import_schema_uri=aiplatform.schema.dataset.ioformat.image.bounding_box,\n",
         ")\n",
@@ -717,7 +713,7 @@
       "outputs": [],
       "source": [
         "job = aiplatform.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + TIMESTAMP,\n",
+        "    display_name=\"salads_\" + UUID,\n",
         "    prediction_type=\"object_detection\",\n",
         "    multi_label=False,\n",
         "    model_type=\"CLOUD\",\n",
@@ -760,7 +756,7 @@
       "source": [
         "model = job.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    model_display_name=\"salads_\" + UUID,\n",
         "    training_fraction_split=0.8,\n",
         "    validation_fraction_split=0.1,\n",
         "    test_fraction_split=0.1,\n",
@@ -790,7 +786,7 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
+        "models = aiplatform.Model.list(filter=\"display_name=salads_\" + UUID)\n",
         "\n",
         "# Get a reference to the Model Service client\n",
         "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -961,7 +957,7 @@
       "outputs": [],
       "source": [
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    job_display_name=\"salads_\" + UUID,\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    machine_type=\"n1-standard-4\",\n",

notebooks/official/bigquery_ml/bqml-online-prediction.ipynb

@@ -32,18 +32,18 @@
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
-        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/bigquery_ml/bqml-online-prediction.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/bigquery_ml/bqml-online-prediction.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/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/community/bigquery_ml/bqml-online-prediction.ipynb\">\n",
+        "    <a href=\"https://github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/bigquery_ml/bqml-online-prediction.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/blob/master/notebooks/community/bigquery_ml/bqml-online-prediction.ipynb\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/bigquery_ml/bqml-online-prediction.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",
@@ -63,7 +63,7 @@
         "\n",
         "### Dataset\n",
         "\n",
-        "The dataset, [available publicly on BigQuery](https://console.cloud.google.com/bigquery?project=bigquery-public-data&d=ga4_obfuscated_sample_ecommerce&p=bigquery-public-data&page=dataset), comes from obfuscated [Google Analytics 4 data](https://support.google.com/analytics/answer/10937659) from the [Google Merchandise Store](https://shop.googlemerchandisestore.com/).\n",
+        "The dataset, <a href=\"https://console.cloud.google.com/bigquery?project=bigquery-public-data&d=ga4_obfuscated_sample_ecommerce&p=bigquery-public-data&page=dataset\" target=\"_blank\">available publicly on BigQuery</a>, comes from obfuscated <a href=\"https://support.google.com/analytics/answer/10937659\" target=\"_blank\">Google Analytics 4 data</a> from the <a href=\"https://shop.googlemerchandisestore.com/\" target=\"_blank\">Google Merchandise Store</a>).\n",
         "\n",
         "### Objective\n",
         "\n",
@@ -95,9 +95,9 @@
         "* Vertex AI\n",
         "\n",
         "\n",
-        "Learn about [BigQuery Pricing](https://cloud.google.com/bigquery/pricing), [BigQuery ML pricing](https://cloud.google.com/bigquery-ml/pricing), [Vertex AI\n",
-        "pricing](https://cloud.google.com/vertex-ai/pricing), and use the [Pricing\n",
-        "Calculator](https://cloud.google.com/products/calculator/)\n",
+        "Learn about <a href=\"https://cloud.google.com/bigquery/pricing\" target=\"_blank\">BigQuery Pricing</a>, <a href=\"https://cloud.google.com/bigquery-ml/pricing\" target=\"_blank\">BigQuery ML pricing</a>, <a href=\"https://cloud.google.com/vertex-ai/pricing\" target=\"_blank\">Vertex AI\n",
+        "pricing</a>, and use the <a href=\"https://cloud.google.com/products/calculator/\" target=\"_blank\">Pricing\n",
+        "Calculator</a>\n",
         "to generate a cost estimate based on your projected usage."
       ]
     },
@@ -128,18 +128,18 @@
         "* virtualenv\n",
         "* Jupyter notebook running in a virtual environment with Python 3\n",
         "\n",
-        "The Google Cloud guide to [Setting up a Python development\n",
-        "environment](https://cloud.google.com/python/setup) and the [Jupyter\n",
-        "installation guide](https://jupyter.org/install) provide detailed instructions\n",
+        "The Google Cloud guide to <a href=\"https://cloud.google.com/python/setup\" target=\"_blank\">Setting up a Python development\n",
+        "environment</a> and the <a href=\"https://jupyter.org/install\" target=\"_blank\">Jupyter\n",
+        "installation guide</a> provide detailed instructions\n",
         "for meeting these requirements. The following steps provide a condensed set of\n",
         "instructions:\n",
         "\n",
-        "1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n",
+        "1. <a href=\"https://cloud.google.com/sdk/docs/\" target=\"_blank\">Install and initialize the Cloud SDK.</a>\n",
         "\n",
-        "1. [Install Python 3.](https://cloud.google.com/python/setup#installing_python)\n",
+        "1. <a href=\"https://cloud.google.com/python/setup#installing_python\" target=\"_blank\">Install Python 3.</a>\n",
         "\n",
-        "1. [Install\n",
-        "   virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n",
+        "1. <a href=\"https://cloud.google.com/python/setup#installing_and_using_virtualenv\" target=\"_blank\">Install\n",
+        "   virtualenv</a>\n",
         "   and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
         "\n",
         "1. To install Jupyter, run `pip3 install jupyter` on the\n",
@@ -234,13 +234,13 @@
         "\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",
+        "1. <a href=\"https://console.cloud.google.com/cloud-resource-manager\" target=\"_blank\">Select or create a Google Cloud project</a>. When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
         "\n",
-        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
+        "1. <a href=\"https://cloud.google.com/billing/docs/how-to/modify-project\" target=\"_blank\">Make sure that billing is enabled for your project</a>.\n",
         "\n",
-        "1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com).\n",
+        "1. <a href=\"https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com\" target=\"_blank\">Enable the Vertex AI API</a>.\n",
         "\n",
-        "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
+        "1. If you are running this notebook locally, you will need to install the <a href=\"https://cloud.google.com/sdk\" target=\"_blank\">Cloud SDK</a>.\n",
         "\n",
         "1. 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",
@@ -267,7 +267,7 @@
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"YOUR-PROJECT-ID\"\n",
+        "PROJECT_ID = \"[YOUR-PROJECT-ID]\"\n",
         "\n",
         "# Get your Google Cloud project ID from gcloud\n",
         "import os\n",
@@ -314,9 +314,9 @@
         "- 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",
+        "You might not be able to 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)."
+        "Learn more about <a href=\"https://cloud.google.com/vertex-ai/docs/general/locations\" target=\"_blank\">Vertex AI regions</a>."
       ]
     },
     {
@@ -339,9 +339,9 @@
         "id": "06571eb4063b"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -352,9 +352,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -380,8 +387,7 @@
         "\n",
         "**Otherwise**, follow these steps:\n",
         "\n",
-        "1. In the Cloud Console, go to the [**Create service account key**\n",
-        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
+        "1. In the Cloud Console, go to the <a href=\"https://console.cloud.google.com/apis/credentials/serviceaccountkey\" target=\"_blank\">**Create service account key** page</a>.\n",
         "\n",
         "2. Click **Create service account**.\n",
         "\n",
@@ -486,7 +492,10 @@
       },
       "outputs": [],
       "source": [
+        "from typing import Union\n",
+        "\n",
         "import google.cloud.aiplatform as vertex_ai\n",
+        "import pandas as pd\n",
         "from google.cloud import bigquery"
       ]
     },
@@ -550,24 +559,17 @@
       "outputs": [],
       "source": [
         "# Wrapper to use BigQuery client to run query/job, return job ID or result as DF\n",
-        "def bq_query(sql):\n",
+        "def run_bq_query(sql: str) -> Union[str, pd.DataFrame]:\n",
         "    \"\"\"\n",
         "    Input: SQL query, as a string, to execute in BigQuery\n",
         "    Returns the query results as a pandas DataFrame, or error, if any\n",
         "    \"\"\"\n",
-        "    # Import Exceptions library to help with dataset error catching\n",
-        "    from google.cloud.exceptions import BadRequest\n",
         "\n",
         "    # Try dry run before executing query to catch any errors\n",
-        "    try:\n",
-        "        job_config = bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)\n",
-        "\n",
-        "        bq_client.query(sql, job_config=job_config)\n",
-        "\n",
-        "    except BadRequest as err:\n",
-        "        print(err)\n",
-        "        return\n",
+        "    job_config = bigquery.QueryJobConfig(dry_run=True, use_query_cache=False)\n",
+        "    bq_client.query(sql, job_config=job_config)\n",
         "\n",
+        "    # If dry run succeeds without errors, proceed to run query\n",
         "    job_config = bigquery.QueryJobConfig()\n",
         "    client_result = bq_client.query(sql, job_config=job_config)\n",
         "\n",
@@ -589,7 +591,7 @@
         "\n",
         "BigQuery ML (BQML) provides the capability to train ML tabular models, such as classification, regression, forecasting, and matrix factorization, in BigQuery using SQL syntax directly. BigQuery ML uses the scalable infrastructure of BigQuery ML so you don't need to set up additional infrastructure for training or batch serving.\n",
         "\n",
-        "Learn more about [BigQuery ML documentation](https://cloud.google.com/bigquery-ml/docs)."
+        "Learn more about <a href=\"https://cloud.google.com/bigquery-ml/docs\" target=\"_blank\">BigQuery ML documentation</a>."
       ]
     },
     {
@@ -600,9 +602,13 @@
       },
       "outputs": [],
       "source": [
-        "BQ_DATASET_NAME = \"ga4_churnprediction\"\n",
+        "BQ_DATASET_NAME = f\"ga4_churnprediction_{UUID}\"\n",
         "\n",
-        "bq_query(f\"\"\"CREATE SCHEMA IF NOT EXISTS {BQ_DATASET_NAME}\"\"\")"
+        "sql_create_dataset = f\"\"\"CREATE SCHEMA IF NOT EXISTS {BQ_DATASET_NAME}\"\"\"\n",
+        "\n",
+        "print(sql_create_dataset)\n",
+        "\n",
+        "run_bq_query(sql_create_dataset)"
       ]
     },
     {
@@ -620,7 +626,7 @@
         "id": "49dd00d5fbe5"
       },
       "source": [
-        "Inpect data that has been pre-processed from [Google Analytics 4 data from the Google Merchandise Store](https://support.google.com/analytics/answer/10937659) so that it can be used for classification. For more information on how this data was prepared, read [this blog post](https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml).\n",
+        "Inpect data that has been pre-processed from <a href=\"https://support.google.com/analytics/answer/10937659\" target=\"_blank\">Google Analytics 4 data from the Google Merchandise Store</a> so that it can be used for classification. For more information on how this data was prepared, read <a href=\"https://cloud.google.com/blog/topics/developers-practitioners/churn-prediction-game-developers-using-google-analytics-4-ga4-and-bigquery-ml\" target=\"_blank\">this blog post</a>.\n",
         "\n",
         "As seen below, each row represents a single user, and the columns represent their demographic features, their aggregated behavioral features in the first 24 hours of visiting the Google Merchandise Store, and the label (whether the user churned or returned any time after the first 24 hours)."
       ]
@@ -641,7 +647,7 @@
         "LIMIT\n",
         "    100\n",
         "\"\"\"\n",
-        "bq_query(sql_inspect)"
+        "run_bq_query(sql_inspect)"
       ]
     },
     {
@@ -662,9 +668,9 @@
         "The query below trains a logistic regression model using BigQuery ML. BigQuery resources are used to train the model.\n",
         "\n",
         "In the `OPTIONS` parameter:\n",
-        "* with `model_registry=\"vertex_ai\"`, the BigQuery ML model will automatically be [registered to Vertex AI Model Registry](https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml), which enables you to view all of your registered models and its versions on Google Cloud in one place.\n",
+        "* with `model_registry=\"vertex_ai\"`, the BigQuery ML model will automatically be <a href=\"https://cloud.google.com/vertex-ai/docs/model-registry/model-registry-bqml\" target=\"_blank\">registered to Vertex AI Model Registry</a>, which enables you to view all of your registered models and its versions on Google Cloud in one place.\n",
         "\n",
-        "* `vertex_ai_model_version_aliases allows you to set aliases to help you keep track of your model version ([documentation](https://cloud.google.com/vertex-ai/docs/model-registry/model-alias))."
+        "* `vertex_ai_model_version_aliases allows you to set aliases to help you keep track of your model version (<a href=\"https://cloud.google.com/vertex-ai/docs/model-registry/model-alias\" target=\"_blank\">documentation</a>)."
       ]
     },
     {
@@ -677,7 +683,7 @@
       "source": [
         "# this cell may take ~1 min to run\n",
         "\n",
-        "BQML_MODEL_NAME = \"bqmlmodelchurn\"\n",
+        "BQML_MODEL_NAME = f\"bqml_model_churn_{UUID}\"\n",
         "\n",
         "sql_train_model_bqml = f\"\"\"\n",
         "CREATE OR REPLACE MODEL {BQ_DATASET_NAME}.{BQML_MODEL_NAME}    \n",
@@ -696,7 +702,7 @@
         "\n",
         "print(sql_train_model_bqml)\n",
         "\n",
-        "bq_query(sql_train_model_bqml)"
+        "run_bq_query(sql_train_model_bqml)"
       ]
     },
     {
@@ -714,7 +720,7 @@
         "id": "2aaaae772f67"
       },
       "source": [
-        "With the model created, you can now evaluate the logistic regression model. Behind the scenes, BigQuery ML automatically [split the data](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method), which makes it easier to quickly train and evaluate models."
+        "With the model created, you can now evaluate the logistic regression model. Behind the scenes, BigQuery ML automatically <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create#data_split_method\" target=\"_blank\">split the data</a>, which makes it easier to quickly train and evaluate models."
       ]
     },
     {
@@ -734,7 +740,7 @@
         "\n",
         "print(sql_evaluate_model)\n",
         "\n",
-        "bq_query(sql_evaluate_model)"
+        "run_bq_query(sql_evaluate_model)"
       ]
     },
     {
@@ -745,7 +751,7 @@
       "source": [
         "These metrics help you understand the performance of the model. \n",
         "\n",
-        "There are various metrics for logistic regression and other model types (full list of metrics can be found in the [documentation](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output))."
+        "There are various metrics for logistic regression and other model types (full list of metrics can be found in the <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-evaluate#mlevaluate_output\" target=\"_blank\">documentation</a>)."
       ]
     },
     {
@@ -765,7 +771,7 @@
       "source": [
         "Make a batch prediction in BigQuery ML on the original training data to check the probability of churn for each of the users, as seen in the `probability` column, with the predicted label under the `predicted_churn` column.\n",
         "\n",
-        "[ML.EXPLAIN_PREDICT](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict) has built-in [Explainable AI](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-xai-overview). This allows you to see the top contributing features to each prediction and interpret how it was computed."
+        "<a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict\" target=\"_blank\">ML.EXPLAIN_PREDICT</a> has built-in <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-xai-overview\" target=\"_blank\">Explainable AI</a>. This allows you to see the top contributing features to each prediction and interpret how it was computed."
       ]
     },
     {
@@ -787,7 +793,7 @@
         "\n",
         "print(sql_explain_predict)\n",
         "\n",
-        "bq_query(sql_explain_predict)"
+        "run_bq_query(sql_explain_predict)"
       ]
     },
     {
@@ -796,7 +802,7 @@
         "id": "fa1f96c0f452"
       },
       "source": [
-        "Since the `top_feature_attributions` is a nested column, you can unnest the array ([documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays)) into separate rows for each of the features. In other words, since ML.EXPLAIN_PREDICT provides the top 5 most important features, using `UNNEST` results in 5 rows per prediction:"
+        "Since the `top_feature_attributions` is a nested column, you can unnest the array (<a href=\"https://cloud.google.com/bigquery/docs/reference/standard-sql/arrays\" target=\"_blank\">documentation</a>) into separate rows for each of the features. In other words, since ML.EXPLAIN_PREDICT provides the top 5 most important features, using `UNNEST` results in 5 rows per prediction:"
       ]
     },
     {
@@ -827,7 +833,7 @@
         "\n",
         "print(sql_explain_predict)\n",
         "\n",
-        "bq_query(sql_explain_predict)"
+        "run_bq_query(sql_explain_predict)"
       ]
     },
     {
@@ -847,7 +853,7 @@
       "source": [
         "When the model was trained in BigQuery ML, the line `model_registry=\"vertex_ai\"` registered the model to Vertex AI Model Registry automatically upon completion.\n",
         "\n",
-        "You can view the model on the [Vertex AI Model Registry page](https://console.cloud.google.com/vertex-ai/models), or use the code below to check that it was successfully registered:"
+        "You can view the model on the <a href=\"https://console.cloud.google.com/vertex-ai/models\" target=\"_blank\">Vertex AI Model Registry page</a>, or use the code below to check that it was successfully registered:"
       ]
     },
     {
@@ -858,12 +864,7 @@
       },
       "outputs": [],
       "source": [
-        "print(f\"BQML_MODEL_NAME = {BQML_MODEL_NAME}\")\n",
-        "\n",
-        "models = vertex_ai.Model.list(\n",
-        "    filter=f\"display_name={BQML_MODEL_NAME}\", order_by=\"update_time\"\n",
-        ")\n",
-        "model = models[0]\n",
+        "model = vertex_ai.Model(model_name=BQML_MODEL_NAME)\n",
         "\n",
         "print(model.gca_resource)"
       ]
@@ -883,7 +884,7 @@
         "id": "b6120dcc1ff6"
       },
       "source": [
-        "While BigQuery ML supports batch prediction with [ML.PREDICT](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict) and [ML.EXPLAIN_PREDICT](https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict), BigQuery ML is not suitable for real-time predictions where you need low latency predictions with potentially high frequency of requests.\n",
+        "While BigQuery ML supports batch prediction with <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict\" target=\"_blank\">ML.PREDICT</a> and <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-explain-predict\" target=\"_blank\">ML.EXPLAIN_PREDICT</a>, BigQuery ML is not suitable for real-time predictions where you need low latency predictions with potentially high frequency of requests.\n",
         "\n",
         "In other words, deploying the BigQuery ML model to an endpoint enables you to do online predictions."
       ]
@@ -906,30 +907,6 @@
         "To deploy your model to an endpoint, you will first need to create an endpoint before you deploy the model to it."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3ce73125dff6"
-      },
-      "outputs": [],
-      "source": [
-        "def create_endpoint(\n",
-        "    project: str,\n",
-        "    display_name: str,\n",
-        "    location: str,\n",
-        "):\n",
-        "    endpoint = vertex_ai.Endpoint.create(\n",
-        "        display_name=display_name,\n",
-        "        project=project,\n",
-        "        location=location,\n",
-        "    )\n",
-        "\n",
-        "    print(endpoint.display_name)\n",
-        "    print(endpoint.resource_name)\n",
-        "    return endpoint"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -938,17 +915,16 @@
       },
       "outputs": [],
       "source": [
-        "endpoint_name = f\"{BQML_MODEL_NAME}-{TIMESTAMP}\"\n",
+        "ENDPOINT_NAME = f\"{BQML_MODEL_NAME}-endpoint\"\n",
         "\n",
-        "print(\n",
-        "    f\"\"\"\n",
-        "PROJECT_ID: {PROJECT_ID},\n",
-        "endpoint_name: {endpoint_name}\n",
-        "REGION: {REGION}\n",
-        "\"\"\"\n",
+        "endpoint = vertex_ai.Endpoint.create(\n",
+        "    display_name=ENDPOINT_NAME,\n",
+        "    project=PROJECT_ID,\n",
+        "    location=REGION,\n",
         ")\n",
         "\n",
-        "create_endpoint(PROJECT_ID, endpoint_name, REGION)"
+        "print(endpoint.display_name)\n",
+        "print(endpoint.resource_name)"
       ]
     },
     {
@@ -966,31 +942,7 @@
         "id": "951ed1693f6b"
       },
       "source": [
-        "List the endpoints to make sure it has successfully been created. You can also view your endpoints on the [Vertex AI Endpoints page](https://console.cloud.google.com/vertex-ai/endpoints?project=polong-contentdev)."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0a9bad8d9ad4"
-      },
-      "outputs": [],
-      "source": [
-        "endpoint = vertex_ai.Endpoint.list(\n",
-        "    # filter=f'display_name={endpoint_name}', # optional: filter by specific endpoint name\n",
-        "    order_by=\"update_time\"\n",
-        ")\n",
-        "endpoint[-1]"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2431a4d28d97"
-      },
-      "source": [
-        "Retrieve the endpoint id so you can use it in the next step."
+        "List the endpoints to make sure it has successfully been created. (You can also view your endpoints on the <a href=\"https://console.cloud.google.com/vertex-ai/endpoints\" target=\"_blank\">Vertex AI Endpoints page</a>)."
       ]
     },
     {
@@ -1001,7 +953,7 @@
       },
       "outputs": [],
       "source": [
-        "endpoint[-1].to_dict()"
+        "endpoint.list()"
       ]
     },
     {
@@ -1019,74 +971,19 @@
         "id": "6a90be5b77a2"
       },
       "source": [
-        "With the model, you can now deploy it to an endpoint. "
+        "With the new endpoint, you can now deploy your model."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "af323ea42c5b"
-      },
-      "outputs": [],
-      "source": [
-        "from typing import Dict, Optional, Sequence, Tuple\n",
-        "\n",
-        "\n",
-        "def deploy_model_with_automatic_resources_sample(\n",
-        "    project,\n",
-        "    location,\n",
-        "    model_name: str,\n",
-        "    endpoint: Optional[vertex_ai.Endpoint] = None,\n",
-        "    deployed_model_display_name: Optional[str] = None,\n",
-        "    traffic_percentage: Optional[int] = 0,\n",
-        "    traffic_split: Optional[Dict[str, int]] = None,\n",
-        "    min_replica_count: int = 1,\n",
-        "    max_replica_count: int = 1,\n",
-        "    metadata: Optional[Sequence[Tuple[str, str]]] = (),\n",
-        "    sync: bool = True,\n",
-        "):\n",
-        "    \"\"\"\n",
-        "    model_name: A fully-qualified model resource name or model ID.\n",
-        "          Example: \"projects/123/locations/us-central1/models/456\" or\n",
-        "          \"456\" when project and location are initialized or passed.\n",
-        "    \"\"\"\n",
-        "\n",
-        "    model = vertex_ai.Model(model_name=model_name)\n",
-        "\n",
-        "    model.deploy(\n",
-        "        endpoint=endpoint,\n",
-        "        deployed_model_display_name=deployed_model_display_name,\n",
-        "        traffic_percentage=traffic_percentage,\n",
-        "        traffic_split=traffic_split,\n",
-        "        min_replica_count=min_replica_count,\n",
-        "        max_replica_count=max_replica_count,\n",
-        "        metadata=metadata,\n",
-        "        sync=sync,\n",
-        "    )\n",
-        "\n",
-        "    model.wait()\n",
-        "\n",
-        "    print(model.display_name)\n",
-        "    print(model.resource_name)\n",
-        "    return"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "9e6763369af4"
+        "id": "c70ecc568ee5"
       },
       "outputs": [],
       "source": [
         "# deploying the model to the endpoint may take 10-15 minutes\n",
-        "deploy_model_with_automatic_resources_sample(\n",
-        "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
-        "    model_name=BQML_MODEL_NAME,\n",
-        "    endpoint=endpoint[-1],\n",
-        ")"
+        "model.deploy(endpoint=endpoint)"
       ]
     },
     {
@@ -1095,7 +992,7 @@
         "id": "c303d779477b"
       },
       "source": [
-        "You can also check on the status of your model by visiting the [Vertex AI Endpoints page](https://console.cloud.google.com/vertex-ai/endpoints)."
+        "You can also check on the status of your model by visiting the <a href=\"https://console.cloud.google.com/vertex-ai/endpoints\" target=\"_blank\">Vertex AI Endpoints page</a>."
       ]
     },
     {
@@ -1168,35 +1065,12 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "2c6093ce9f8a"
+        "id": "b4839f31d2f8"
       },
       "outputs": [],
       "source": [
-        "def endpoint_predict_sample(\n",
-        "    project: str, location: str, instances: list, endpoint: str\n",
-        "):\n",
-        "    endpoint = vertex_ai.Endpoint(endpoint)\n",
-        "\n",
-        "    prediction = endpoint.predict(instances=instances)\n",
-        "    return prediction"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "0c41fd6eeb6f"
-      },
-      "outputs": [],
-      "source": [
-        "prediction_response = endpoint_predict_sample(\n",
-        "    project=PROJECT_ID,\n",
-        "    location=REGION,\n",
-        "    instances=df_sample_requests_list,\n",
-        "    endpoint=endpoint[-1].name,\n",
-        ")\n",
-        "\n",
-        "prediction_response"
+        "prediction = endpoint.predict(df_sample_requests_list)\n",
+        "print(prediction)"
       ]
     },
     {
@@ -1216,7 +1090,7 @@
       },
       "outputs": [],
       "source": [
-        "prediction_response.predictions"
+        "prediction.predictions"
       ]
     },
     {
@@ -1227,8 +1101,8 @@
       "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",
+        "To clean up all Google Cloud resources used in this project, you can <a href=\"https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects\" target=\"_blank\">delete the Google Cloud\n",
+        "project</a> you used for the tutorial.\n",
         "\n",
         "Otherwise, you can delete the individual resources you created in this tutorial:"
       ]
@@ -1241,18 +1115,12 @@
       },
       "outputs": [],
       "source": [
-        "# MODEL_ID = model.name\n",
+        "# Undeploy model from endpoint and delete endpoint\n",
+        "endpoint.undeploy_all()\n",
+        "endpoint.delete()\n",
         "\n",
-        "ENDPOINT_ID = int(endpoint[-1].name)\n",
-        "\n",
-        "# Undeploy model from endpoint\n",
-        "endpoint[-1].undeploy_all()\n",
-        "\n",
-        "# Delete endpoint resource\n",
-        "! gcloud ai endpoints delete $ENDPOINT_ID --quiet --region $REGION\n",
-        "\n",
-        "# Delete BigQuery ML model\n",
-        "! bq rm -f --model $PROJECT_ID\\:$BQ_DATASET_NAME\\.$BQML_MODEL_NAME"
+        "# Delete BigQuery dataset, including the BigQuery ML model\n",
+        "! bq rm -r -f $PROJECT_ID:$BQ_DATASET_NAME"
       ]
     }
   ],

notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb

@@ -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/custom-tabular-bq-managed-dataset.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/custom-tabular-bq-managed-dataset.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",
@@ -263,7 +263,7 @@
         "\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",
+        "3. [Enable the following APIs: Vertex AI API, Cloud Resource Manager API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,cloudresourcemanager.googleapis.com).\n",
         "\n",
         "4. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
         "\n",

notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb

@@ -8,7 +8,7 @@
       },
       "outputs": [],
       "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# Copyright 2021 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",
@@ -54,13 +54,31 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "tvgnzT1CKxrO"
+        "id": "d975c5729f18"
       },
       "source": [
         "## Overview\n",
         "\n",
-        "As a Data Scientist, you want to be able to reuse code path (data preprocessing, feature engineering etc...) that others within your team have written to simplify and standardize all the complex data wrangling. \n",
+        "As a Data Scientist, you want to be able to reuse code path (data preprocessing, feature engineering etc...) that others within your team have written to simplify and standardize all the complex data wrangling. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3a0f8061b9c1"
+      },
+      "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."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
         "### Dataset\n",
         "\n",
         "This dataset is the UCI News Aggregator Data Set which contains 422,937 news collected between March 10th, 2014 and August 10th, 2014. Below are example records from the dataset:\n",
@@ -72,13 +90,15 @@
         "|2  |Fed's Charles Plosser sees high bar for change in pace of tapering   |http://www.livemint.com/Politics/H2EvwJSK2VE6OF7iK1g3PP/Feds-Charles-Plosser-sees-high-bar-for-change-in-pace-of-ta.html   |Livemint         |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.livemint.com   |1394470371207|\n",
         "|3  |US open: Stocks fall after Fed official hints at accelerated tapering|http://www.ifamagazine.com/news/us-open-stocks-fall-after-fed-official-hints-at-accelerated-tapering-294436                |IFA Magazine     |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.ifamagazine.com|1394470371550|\n",
         "|4  |Fed risks falling 'behind the curve', Charles Plosser says           |http://www.ifamagazine.com/news/fed-risks-falling-behind-the-curve-charles-plosser-says-294430                             |IFA Magazine     |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.ifamagazine.com|1394470371793|\n",
-        "|5  |Fed's Plosser: Nasty Weather Has Curbed Job Growth                   |http://www.moneynews.com/Economy/federal-reserve-charles-plosser-weather-job-growth/2014/03/10/id/557011                   |Moneynews        |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.moneynews.com  |1394470372027|\n",
-        "\n",
-        "\n",
-        "### Objective\n",
-        "\n",
-        "In this notebook, you learn how to integrate preprocessing code in a Vertex AI experiments. Also you will build the experiment lineage lets you record, analyze, debug, and audit metadata and artifacts produced along your ML journey.\n",
-        "\n",
+        "|5  |Fed's Plosser: Nasty Weather Has Curbed Job Growth                   |http://www.moneynews.com/Economy/federal-reserve-charles-plosser-weather-job-growth/2014/03/10/id/557011                   |Moneynews        |b       |ddUyU0VZz0BRneMioxUPQVP6sIxvM|www.moneynews.com  |1394470372027|"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5e2eba58ad71"
+      },
+      "source": [
         "### Costs \n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",
@@ -150,7 +170,7 @@
       "source": [
         "### Install additional packages\n",
         "\n",
-        "Install additional package dependencies not installed in your notebook environment, such as TensorFlow or Vertex AI SDK. Use the latest major GA version of each package."
+        "Install additional package dependencies not installed in your notebook environment,TensorFlow or Vertex AI SDK. Use the latest major GA version of each package."
       ]
     },
     {
@@ -175,7 +195,7 @@
         "    USER_FLAG = \"--user\"\n",
         "\n",
         "! pip3 install {USER_FLAG} --upgrade joblib fsspec gcsfs scikit-learn -q\n",
-        "! pip3 install {USER_FLAG} --force-reinstall 'google-cloud-aiplatform>=1.15' -q"
+        "! pip install {USER_FLAG} --upgrade google-cloud-aiplatform -q"
       ]
     },
     {
@@ -208,21 +228,14 @@
         "    app.kernel.do_shutdown(True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "lWEdiXsJg0XY"
-      },
-      "source": [
-        "## Before you begin"
-      ]
-    },
     {
       "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",
@@ -233,7 +246,7 @@
         "\n",
         "1. [Enable APIs](https://console.cloud.google.com/flows/enableapi?apiid=cloudresourcemanager.googleapis.com,aiplatform.googleapis.com).\n",
         "\n",
-        "1. If you are running this notebook locally, you will need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
+        "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
         "\n",
         "1. 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",
@@ -343,9 +356,9 @@
         "id": "06571eb4063b"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -356,9 +369,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -370,7 +390,7 @@
         "### Authenticate your Google Cloud account\n",
         "\n",
         "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
-        "authenticated. Skip this step."
+        "authenticated."
       ]
     },
     {
@@ -484,7 +504,7 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
         "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
@@ -537,17 +557,6 @@
         "### Set project folder"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "AARD6Fsr-DSi"
-      },
-      "outputs": [],
-      "source": [
-        "DATA_PATH = \"data\""
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -556,6 +565,7 @@
       },
       "outputs": [],
       "source": [
+        "DATA_PATH = \"data\"\n",
         "!mkdir -m 777 -p {DATA_PATH}"
       ]
     },
@@ -568,17 +578,6 @@
         "### Get the data"
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3V6W2nIo9FtL"
-      },
-      "outputs": [],
-      "source": [
-        "DATASET_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00359/NewsAggregatorDataset.zip\""
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -587,6 +586,7 @@
       },
       "outputs": [],
       "source": [
+        "DATASET_URL = \"https://archive.ics.uci.edu/ml/machine-learning-databases/00359/NewsAggregatorDataset.zip\"\n",
         "!wget --no-parent {DATASET_URL} --directory-prefix={DATA_PATH}\n",
         "!mkdir -m 777 -p {DATA_PATH}/temp {DATA_PATH}/raw\n",
         "!unzip {DATA_PATH}/*.zip -d {DATA_PATH}/temp\n",
@@ -662,7 +662,7 @@
         "# Experiments\n",
         "TASK = \"classification\"\n",
         "MODEL_TYPE = \"naivebayes\"\n",
-        "EXPERIMENT_NAME = f\"{TASK}-{MODEL_TYPE}-{TIMESTAMP}\"\n",
+        "EXPERIMENT_NAME = f\"{TASK}-{MODEL_TYPE}-{UUID}\"\n",
         "EXPERIMENT_RUN_NAME = \"run-1\"\n",
         "\n",
         "# Preprocessing\n",
@@ -690,7 +690,7 @@
         "FEATURES = \"title\"\n",
         "TEST_SIZE = 0.2\n",
         "SEED = 8\n",
-        "TRAINED_MODEL_URI = f\"{BUCKET_URI}/deliverables/{TIMESTAMP}\"\n",
+        "TRAINED_MODEL_URI = f\"{BUCKET_URI}/deliverables/{UUID}\"\n",
         "MODEL_NAME = f\"{EXPERIMENT_NAME}-model\""
       ]
     },
@@ -800,7 +800,7 @@
       "source": [
         "#### Create a Dataset Metadata Artifact\n",
         "\n",
-        "First you create the Dataset artifact to track the dataset resource in the Vertex AI ML Metadata and create the experiment lineage."
+        "First you create the Dataset artifact to track the dataset resource in the Vertex ML Metadata and create the experiment lineage."
       ]
     },
     {
@@ -839,7 +839,6 @@
         "Preprocess module\n",
         "\"\"\"\n",
         "\n",
-        "import string\n",
         "\n",
         "import pandas as pd\n",
         "\n",
@@ -869,7 +868,10 @@
       "source": [
         "#### Add the `preprocessing` Execution\n",
         "\n",
-        "You add the preprocessing step to track its execution in the lineage associated to Vertex AI Experiment. "
+        "Vertex AI Experiments supports tracking both executions and artifacts. Executions are steps in an ML workflow that can include but are not limited to data preprocessing, training, and model evaluation. Executions can consume artifacts such as datasets and produce artifacts such as models.\n",
+        "\n",
+        "You add the preprocessing step to track its execution in the lineage associated to Vertex AI Experiment. \n",
+        "For Vertex AI, the parameters are passed inside the message field which we see in the logs. These structures of the logs are predefined."
       ]
     },
     {
@@ -943,7 +945,16 @@
       "source": [
         "#### Create model training module\n",
         "\n",
-        "Below the training module."
+        "Below the training module.\n",
+        "\n",
+        "**get_training_split :** It takes parameters like  x(The data to be split), y(The labels to be split), test_size(The proportion of the data to be reserved for testing) and random_state(The seed used by the random number generator).\n",
+        "This function return training data, testing data , The training labels and The testing labels.\n",
+        "\n",
+        "**get_pipeline :** It return's the model.\n",
+        "\n",
+        "**train_pipeline :** It train the model by using model, training data, training lables and return's the trained model.\n",
+        "\n",
+        "**evaluate_model :** It evaluate the model and return the accuracy of the model.\n"
       ]
     },
     {
@@ -1151,6 +1162,15 @@
         "    exc.assign_output_artifacts([model])"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e595c893de8d"
+      },
+      "source": [
+        "### Stop Experiment run"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1170,7 +1190,7 @@
       "source": [
         "### Visualize Experiment Lineage\n",
         "\n",
-        "Below you will get the link to Vertex AI Metadata UI in the console that will show the experiment lineage."
+        "Below you get the link to Vertex AI Metadata UI in the console that show the experiment lineage."
       ]
     },
     {
@@ -1208,17 +1228,8 @@
       "source": [
         "# Delete experiment\n",
         "exp = vertex_ai.Experiment(EXPERIMENT_NAME)\n",
-        "exp.delete()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "gW8Ddbr8xaKp"
-      },
-      "outputs": [],
-      "source": [
+        "exp.delete()\n",
+        "\n",
         "# Delete model\n",
         "model_list = vertex_ai.Model.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
         "for model in model_list:\n",
@@ -1230,22 +1241,15 @@
         "        filter=f'display_name=\"{dataset_name}\"'\n",
         "    )\n",
         "    for dataset in dataset_list:\n",
-        "        dataset.delete()"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "sx_vKniMq9ZX"
-      },
-      "outputs": [],
-      "source": [
+        "        dataset.delete()\n",
+        "\n",
         "# Delete Cloud Storage objects that were created\n",
         "delete_bucket = True\n",
         "\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
-        "    ! gsutil -m rm -r $BUCKET_URI"
+        "    ! gsutil -m rm -r $BUCKET_URI\n",
+        "\n",
+        "!rm -Rf {DATA_PATH}"
       ]
     },
     {

notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb

@@ -29,7 +29,7 @@
         "id": "title"
       },
       "source": [
-        "# Vertex SDK: AutoML training tabular binary classification model for batch explanation\n",
+        "# Vertex AI SDK: AutoML training tabular binary classification model for batch explanation\n",
         "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
@@ -65,17 +65,6 @@
         "This tutorial demonstrates how to use the Vertex AI SDK to create tabular binary classification models and do batch prediction with explanation using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:bank,lbn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . 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."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -108,6 +97,17 @@
         "* 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": "7a4881cf39a4"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the Bank Marketing. This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -136,7 +136,7 @@
       "source": [
         "### Set up your local development environment\n",
         "\n",
-        "If you are using Colab or Vertex Workbench Notebook, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
+        "If you are using Colab or Vertex AI Workbench Notebook, 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",
@@ -322,7 +322,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -331,9 +334,8 @@
         "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."
+        "#### UUID\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 the uuid onto the name of resources you create in this tutorial."
       ]
     },
     {
@@ -344,9 +346,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\n",
+        "# Generate a uuid of length 8\n",
+        "def generate_uuid():\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=8))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -357,7 +366,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Workbench AI Notebooks**, your environment is already\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
         "authenticated. Skip this step.\n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
@@ -440,7 +449,7 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
         "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
@@ -453,7 +462,7 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
         "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
       ]
     },
@@ -583,7 +592,7 @@
       "source": [
         "#### Quick peek at your data\n",
         "\n",
-        "You will use a version of the Bank Marketing dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
+        "You use a version of the Bank Marketing dataset that is stored in a public Cloud Storage bucket, using a CSV index file.\n",
         "\n",
         "Start by doing a quick peek at the data. You count the number of examples by counting the number of rows in the CSV index file  (`wc -l`) and then peek at the first few rows.\n",
         "\n",
@@ -637,7 +646,7 @@
       "outputs": [],
       "source": [
         "dataset = aip.TabularDataset.create(\n",
-        "    display_name=\"Bank Marketing\" + \"_\" + TIMESTAMP, gcs_source=[IMPORT_FILE]\n",
+        "    display_name=\"Bank Marketing\" + \"_\" + UUID, gcs_source=[IMPORT_FILE]\n",
         ")\n",
         "\n",
         "print(dataset.resource_name)"
@@ -688,7 +697,7 @@
       "outputs": [],
       "source": [
         "dag = aip.AutoMLTabularTrainingJob(\n",
-        "    display_name=\"bank_\" + TIMESTAMP,\n",
+        "    display_name=\"bank_\" + UUID,\n",
         "    optimization_prediction_type=\"classification\",\n",
         "    optimization_objective=\"minimize-log-loss\",\n",
         ")\n",
@@ -730,7 +739,7 @@
       "source": [
         "model = dag.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"bank_\" + TIMESTAMP,\n",
+        "    model_display_name=\"bank_\" + UUID,\n",
         "    training_fraction_split=0.6,\n",
         "    validation_fraction_split=0.2,\n",
         "    test_fraction_split=0.2,\n",
@@ -784,7 +793,7 @@
       "source": [
         "### Make test items\n",
         "\n",
-        "You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "You use synthetic data as a test data items. Don't be concerned that we are using synthetic data."
       ]
     },
     {
@@ -852,11 +861,11 @@
       "outputs": [],
       "source": [
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"bank_\" + TIMESTAMP,\n",
+        "    job_display_name=\"bank_\" + UUID,\n",
         "    gcs_source=gcs_input_uri,\n",
         "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"csv\",\n",
-        "    predictions_format=\"csv\",\n",
+        "    predictions_format=\"jsonl\",\n",
         "    generate_explanation=True,\n",
         "    sync=False,\n",
         ")\n",
@@ -952,6 +961,7 @@
         "# Set this to true only if you'd like to delete your bucket\n",
         "delete_bucket = False\n",
         "\n",
+        "dataset.delete()\n",
         "model.delete()\n",
         "batch_predict_job.delete()\n",
         "\n",

notebooks/official/feature_store/sdk-feature-store.ipynb

@@ -29,6 +29,8 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
+        "# Online and Batch predictions using Vertex AI Feature Store\n",
+        "\n",
         "<table align=\"left\">\n",
         "  <td>\n",
         "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/feature_store/sdk-feature-store.ipynb\">\n",
@@ -51,19 +53,22 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "tvgnzT1CKxrO"
+        "id": "c4aaea3bab5e"
       },
       "source": [
         "## Overview\n",
         "\n",
         "This notebook introduces Vertex AI Feature Store, a managed cloud service for machine learning engineers and data scientists to store, serve, manage and share machine learning features at a large scale.\n",
         "\n",
-        "This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\n",
-        "\n",
-        "### Dataset\n",
-        "\n",
-        "This notebook uses a movie recommendation dataset as an example throughout all the sessions. The task is to train a model to predict if a user is going to watch a movie and serve this model online. \n",
-        "\n",
+        "This notebook assumes that you understand basic Google Cloud concepts such as [Project](https://cloud.google.com/storage/docs/projects), [Storage](https://cloud.google.com/storage) and [Vertex AI](https://cloud.google.com/vertex-ai/docs). Some machine learning knowledge is also helpful but not required.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "71779c8088bf"
+      },
+      "source": [
         "### Objective\n",
         "\n",
         "In this notebook, you will 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.\n",
@@ -79,8 +84,26 @@
         "- Create featurestore, entity type, and feature resources.\n",
         "- Import feature data into `Vertex AI Feature Store` resource.\n",
         "- Serve online prediction requests using the imported features.\n",
-        "- Access imported features in offline jobs, such as training jobs.\n",
+        "- Access imported features in offline jobs, such as training jobs."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "55e01a856f57"
+      },
+      "source": [
+        "### Dataset\n",
         "\n",
+        "This notebook uses a movie recommendation dataset as an example throughout all the sessions. The task is to train a model to predict if a user is going to watch a movie and serve this model online."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
         "### Costs \n",
         "\n",
         "This tutorial uses billable components of Google Cloud:\n",
@@ -262,7 +285,15 @@
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+        "import os\n",
+        "\n",
+        "PROJECT_ID = \"\"\n",
+        "\n",
+        "# Get your Google Cloud project ID from gcloud\n",
+        "if not os.getenv(\"IS_TESTING\"):\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)"
       ]
     },
     {
@@ -275,10 +306,7 @@
       "source": [
         "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
         "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\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)"
+        "print(\"Project ID: \", PROJECT_ID)"
       ]
     },
     {
@@ -320,7 +348,9 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type:\"string\"}\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -329,9 +359,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -342,9 +372,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -441,7 +478,7 @@
       "source": [
         "from google.cloud.aiplatform import Feature, Featurestore\n",
         "\n",
-        "FEATURESTORE_ID = \"movie_prediction\"\n",
+        "FEATURESTORE_ID = \"movie_prediction\" + UUID\n",
         "INPUT_CSV_FILE = \"gs://cloud-samples-data-us-central1/vertex-ai/feature-store/datasets/movie_prediction.csv\"\n",
         "ONLINE_STORE_FIXED_NODE_COUNT = 1"
       ]

notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.ipynb

@@ -32,17 +32,24 @@
         "# Vertex AI: Vertex AI Migration: Hyperparameter Tuning\n",
         "\n",
         "<table align=\"left\">\n",
+        "\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/UJ11%20Vertex%20SDK%20Hyperparameter%20Tuning.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.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/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ11%20Vertex%20SDK%20Hyperparameter%20Tuning.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.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://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ11 Vertex SDK Hyperparameter Tuning.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>\n",
         "<br/><br/><br/>"
       ]
@@ -55,7 +62,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset you will use in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD."
+        "The dataset used for this tutorial is the [Boston Housing Prices dataset](https://www.cs.toronto.edu/~delve/data/boston/bostonDetail.html). The version of the dataset you use in this tutorial is built into TensorFlow. The trained model predicts the median price of a house in units of 1K USD."
       ]
     },
     {
@@ -138,7 +145,7 @@
         "else:\n",
         "    USER_FLAG = \"\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
+        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q"
       ]
     },
     {
@@ -158,7 +165,7 @@
       },
       "outputs": [],
       "source": [
-        "! pip3 install -U google-cloud-storage $USER_FLAG"
+        "! pip3 install -U google-cloud-storage $USER_FLAG -q"
       ]
     },
     {
@@ -212,7 +219,7 @@
         "\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",
+        "4. If you are running this notebook locally, you 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",
@@ -285,7 +292,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -294,9 +304,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -307,9 +317,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -320,7 +337,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already authenticated.\n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
         "\n",
@@ -355,8 +372,11 @@
         "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 on Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\n",
         "    if \"google.colab\" in sys.modules:\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
@@ -392,7 +412,8 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -403,8 +424,9 @@
       },
       "outputs": [],
       "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -424,7 +446,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -444,7 +466,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gsutil ls -al $BUCKET_URI"
       ]
     },
     {
@@ -489,7 +511,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
+        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -511,7 +533,7 @@
         "\n",
         "Learn more [here](https://cloud.google.com/vertex-ai/docs/general/locations#accelerators) hardware accelerator support for your region\n",
         "\n",
-        "*Note*: TF releases before 2.3 for GPU support will fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 -- which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
+        "*Note*: TF releases before 2.3 for GPU support  fail to load the custom model in this tutorial. It is a known issue and fixed in TF 2.3 -- which is caused by static graph ops that are generated in the serving function. If you encounter this issue on your own custom models, use a container image for TF 2.3 with GPU support."
       ]
     },
     {
@@ -522,6 +544,8 @@
       },
       "outputs": [],
       "source": [
+        "import os\n",
+        "\n",
         "if os.getenv(\"IS_TESTING_TRAIN_GPU\"):\n",
         "    TRAIN_GPU, TRAIN_NGPU = (\n",
         "        aip.gapic.AcceleratorType.NVIDIA_TESLA_K80,\n",
@@ -605,7 +629,7 @@
         "\n",
         "Next, set the machine type to use for training and prediction.\n",
         "\n",
-        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you will use for for training and prediction.\n",
+        "- Set the variables `TRAIN_COMPUTE` and `DEPLOY_COMPUTE` to configure  the compute resources for the VMs you use for for training and prediction.\n",
         " - `machine type`\n",
         "     - `n1-standard`: 3.75GB of memory per vCPU.\n",
         "     - `n1-highmem`: 6.5GB of memory per vCPU\n",
@@ -657,7 +681,7 @@
         "\n",
         "#### Package layout\n",
         "\n",
-        "Before you start the training, you will look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
+        "Before you start the training, you look at how a Python package is assembled for a custom training job. When unarchived, the package contains the following directory/file layout.\n",
         "\n",
         "- PKG-INFO\n",
         "- README.md\n",
@@ -673,7 +697,7 @@
         "\n",
         "#### Package Assembly\n",
         "\n",
-        "In the following cells, you will assemble the training package."
+        "In the following cells, you assemble the training package."
       ]
     },
     {
@@ -721,7 +745,7 @@
         "- Build a DNN model.\n",
         "- The number of units per dense layer and learning rate hyperparameter values are used during the build and compile of the model.\n",
         "- A definition of a callback `HPTCallback` which obtains the validation loss at the end of each epoch (`on_epoch_end()`) and reports it to the hyperparameter tuning service using `hpt.report_hyperparameter_tuning_metric()`.\n",
-        "- Train the model with the `fit()` method and specify a callback which will report the validation loss back to the hyperparameter tuning service."
+        "- Train the model with the `fit()` method and specify a callback which report the validation loss back to the hyperparameter tuning service."
       ]
     },
     {
@@ -854,7 +878,7 @@
         "! rm -f custom.tar custom.tar.gz\n",
         "! tar cvf custom.tar custom\n",
         "! gzip custom.tar\n",
-        "! gsutil cp custom.tar.gz $BUCKET_NAME/trainer_boston.tar.gz"
+        "! gsutil cp custom.tar.gz $BUCKET_URI/trainer_boston.tar.gz"
       ]
     },
     {
@@ -885,7 +909,7 @@
         "\n",
         "Now define the machine specification for your custom training job. This tells Vertex what type of machine instance to provision for the training.\n",
         "  - `machine_type`: The type of GCP instance to provision -- e.g., n1-standard-8.\n",
-        "  - `accelerator_type`: The type, if any, of hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you are using a GPU; otherwise you will use a CPU.\n",
+        "  - `accelerator_type`: The type, if any, of hardware accelerator. In this tutorial if you previously set the variable `TRAIN_GPU != None`, you are using a GPU; otherwise you use a CPU.\n",
         "  - `accelerator_count`: The number of accelerators."
       ]
     },
@@ -943,7 +967,7 @@
       "source": [
         "### Define the worker pool specification\n",
         "\n",
-        "Next, you define the worker pool specification for your custom training job. The worker pool specification will consist of the following:\n",
+        "Next, you define the worker pool specification for your custom training job. The worker pool specification  consist of the following:\n",
         "\n",
         "- `replica_count`: The number of instances to provision of this machine type.\n",
         "- `machine_spec`: The hardware specification.\n",
@@ -955,11 +979,11 @@
         "\n",
         "-`executor_image_spec`: This is the docker image which is configured for your custom training job.\n",
         "\n",
-        "-`package_uris`: This is a list of the locations (URIs) of your python training packages to install on the provisioned instance. The locations need to be in a Cloud Storage bucket. These can be either individual python files or a zip (archive) of an entire package. In the later case, the job service will unzip (unarchive) the contents into the docker image.\n",
+        "-`package_uris`: This is a list of the locations (URIs) of your python training packages to install on the provisioned instance. The locations need to be in a Cloud Storage bucket. These can be either individual python files or a zip (archive) of an entire package. In the later case, the job service  unzip (unarchive) the contents into the docker image.\n",
         "\n",
-        "-`python_module`: The Python module (script) to invoke for running the custom training job. In this example, you will be invoking `trainer.task.py` -- note that it was not neccessary to append the `.py` suffix.\n",
+        "-`python_module`: The Python module (script) to invoke for running the custom training job. In this example, you  be invoking `trainer.task.py` -- note that it was not neccessary to append the `.py` suffix.\n",
         "\n",
-        "-`args`: The command line arguments to pass to the corresponding Pythom module. In this example, you will be setting:\n",
+        "-`args`: The command line arguments to pass to the corresponding Pythom module. In this example, you  be setting:\n",
         "  - `\"--model-dir=\" + MODEL_DIR` : The Cloud Storage location where to store the model artifacts. There are two ways to tell the training script where to save the model artifacts:\n",
         "      - direct: You pass the Cloud Storage location as a command line argument to your training script (set variable `DIRECT = True`), or\n",
         "      - indirect: The service passes the Cloud Storage location as the environment variable `AIP_MODEL_DIR` to your training script (set variable `DIRECT = False`). In this case, you tell the service the model artifact location in the job specification.\n",
@@ -979,8 +1003,8 @@
       },
       "outputs": [],
       "source": [
-        "JOB_NAME = \"custom_job_\" + TIMESTAMP\n",
-        "MODEL_DIR = \"{}/{}\".format(BUCKET_NAME, JOB_NAME)\n",
+        "JOB_NAME = \"custom_job_\" + UUID\n",
+        "MODEL_DIR = \"{}/{}\".format(BUCKET_URI, JOB_NAME)\n",
         "\n",
         "if not TRAIN_NGPU or TRAIN_NGPU < 2:\n",
         "    TRAIN_STRATEGY = \"single\"\n",
@@ -1012,7 +1036,7 @@
         "        \"disk_spec\": disk_spec,\n",
         "        \"python_package_spec\": {\n",
         "            \"executor_image_uri\": TRAIN_IMAGE,\n",
-        "            \"package_uris\": [BUCKET_NAME + \"/trainer_boston.tar.gz\"],\n",
+        "            \"package_uris\": [BUCKET_URI + \"/trainer_boston.tar.gz\"],\n",
         "            \"python_module\": \"trainer.task\",\n",
         "            \"args\": CMDARGS,\n",
         "        },\n",
@@ -1051,9 +1075,7 @@
       },
       "outputs": [],
       "source": [
-        "job = aip.CustomJob(\n",
-        "    display_name=\"boston_\" + TIMESTAMP, worker_pool_specs=worker_pool_spec\n",
-        ")\n",
+        "job = aip.CustomJob(display_name=\"boston_\" + UUID, worker_pool_specs=worker_pool_spec)\n",
         "\n",
         "# print(job)"
       ]
@@ -1085,7 +1107,7 @@
         "from google.cloud.aiplatform import hyperparameter_tuning as hpt\n",
         "\n",
         "hpt_job = aip.HyperparameterTuningJob(\n",
-        "    display_name=\"boston_\" + TIMESTAMP,\n",
+        "    display_name=\"boston_\" + UUID,\n",
         "    custom_job=job,\n",
         "    metric_spec={\n",
         "        \"val_loss\": \"minimize\",\n",
@@ -1155,7 +1177,7 @@
       "source": [
         "### Display the hyperparameter tuning job trial results\n",
         "\n",
-        "After the hyperparameter tuning job has completed, the property `trials` will return the results for each trial."
+        "After the hyperparameter tuning job has completed, the property `trials`  return the results for each trial."
       ]
     },
     {
@@ -1350,7 +1372,7 @@
         "        print(e)\n",
         "\n",
         "    if \"BUCKET_NAME\" in globals():\n",
-        "        ! gsutil rm -r $BUCKET_NAME"
+        "        ! gsutil rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.ipynb

@@ -32,19 +32,64 @@
         "# Vertex AI: Vertex AI Migration: AutoML Tabular Binary Classification\n",
         "\n",
         "<table align=\"left\">\n",
+        "\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/UJ4%20Vertex%20SDK%20AutoML%20Tabular%20Binary%20Classification.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.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/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ4%20Vertex%20SDK%20AutoML%20Tabular%20Binary%20Classification.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ4 Vertex SDK AutoML Tabular Binary Classification.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",
-        "</table>\n",
-        "<br/><br/><br/>"
+        "  <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/UJ4 Vertex SDK AutoML Tabular Binary Classification.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": "fb82f94bbbc7"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "\n",
+        "This tutorial demonstrates how to use the Vertex AI SDK to create tabular binary classification models and do online prediction using a Google Cloud [AutoML](https://cloud.google.com/vertex-ai/docs/start/automl-users) model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9f80bba45dd5"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "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. 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 and resources:\n",
+        "\n",
+        "- Vertex AI managed Datasets\n",
+        "- Vertex AI Training\n",
+        "- Vertex AI Endpoints\n",
+        "- Vertex AI prediction\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Create a Vertex `Dataset` resource.\n",
+        "- Train the model.\n",
+        "- View the model evaluation.\n",
+        "- Deploy the `Model` resource to a serving `Endpoint` resource.\n",
+        "- Make a prediction.\n",
+        "- Undeploy the `Model`"
       ]
     },
     {
@@ -55,7 +100,7 @@
       "source": [
         "### Dataset\n",
         "\n",
-        "The dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . 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 dataset used for this tutorial is the [Bank Marketing](https://pantheon.corp.google.com/storage/browser/_details/cloud-ml-tables-data/bank-marketing.csv) . This dataset does not require any feature engineering. The version of the dataset you use in this tutorial is stored in a public Cloud Storage bucket."
       ]
     },
     {
@@ -86,29 +131,38 @@
       "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",
+        "**If you are using Colab or Vertex AI Workbench Notebooks**, your environment already meets\n",
+        "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",
+        "**Otherwise**, make sure your environment meets this notebook's requirements.\n",
+        "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",
+        "* The Google Cloud 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",
+        "The Google Cloud guide to [Setting up a Python development\n",
+        "environment](https://cloud.google.com/python/setup) and the [Jupyter\n",
+        "installation guide](https://jupyter.org/install) provide detailed instructions\n",
+        "for meeting these requirements. The following steps provide a condensed set of\n",
+        "instructions:\n",
         "\n",
-        "1. [Install and initialize the SDK](https://cloud.google.com/sdk/docs/).\n",
+        "1. [Install and initialize the Cloud SDK.](https://cloud.google.com/sdk/docs/)\n",
         "\n",
-        "2. [Install Python 3](https://cloud.google.com/python/setup#installing_python).\n",
+        "1. [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",
+        "1. [Install\n",
+        "   virtualenv](https://cloud.google.com/python/setup#installing_and_using_virtualenv)\n",
+        "   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",
+        "1. To install Jupyter, run `pip3 install jupyter` on the\n",
+        "command-line in a terminal shell.\n",
         "\n",
-        "5. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
+        "1. 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"
+        "1. Open this notebook in the Jupyter Notebook Dashboard."
       ]
     },
     {
@@ -119,7 +173,7 @@
       "source": [
         "## Installation\n",
         "\n",
-        "Install the latest version of Vertex SDK for Python."
+        "Install the following packages required to execute this notebook. "
       ]
     },
     {
@@ -132,33 +186,18 @@
       "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",
+        "# The Vertex AI Workbench Notebook product has specific requirements\n",
+        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
+        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\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* and *tensorflow* libraries as well."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "install_storage"
-      },
-      "outputs": [],
-      "source": [
-        "! pip3 install -U google-cloud-storage tensorflow $USER_FLAG"
+        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
+        "USER_FLAG = \"\"\n",
+        "if IS_WORKBENCH_NOTEBOOK:\n",
+        "    USER_FLAG = \"--user\"\n",
+        "\n",
+        "! pip3 install --upgrade google-cloud-aiplatform fsspec gcsfs $USER_FLAG"
       ]
     },
     {
@@ -169,7 +208,7 @@
       "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."
+        "After you install the additional packages, you need to restart the notebook kernel so it can find the packages."
       ]
     },
     {
@@ -180,6 +219,7 @@
       },
       "outputs": [],
       "source": [
+        "# Automatically restart kernel after installs\n",
         "import os\n",
         "\n",
         "if not os.getenv(\"IS_TESTING\"):\n",
@@ -193,31 +233,38 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "before_you_begin:nogpu"
+        "id": "c27795e4f4a1"
       },
       "source": [
         "## Before you begin\n",
         "\n",
-        "### GPU runtime\n",
-        "\n",
-        "This tutorial does not require a GPU runtime.\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 $300 free credit towards your compute/storage costs.\n",
         "\n",
-        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
+        "1. [Make sure that billing is enabled for your project](https://cloud.google.com/billing/docs/how-to/modify-project).\n",
         "\n",
-        "3. [Enable the following APIs: Vertex AI APIs, Compute Engine APIs, and Cloud Storage.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component,storage-component.googleapis.com)\n",
+        "1. [Enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). \n",
         "\n",
-        "4. If you are running this notebook locally, you will need to install the [Cloud SDK]((https://cloud.google.com/sdk)).\n",
+        "1. If you are running this notebook locally, you need to install the [Cloud SDK](https://cloud.google.com/sdk).\n",
         "\n",
-        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
+        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
         "Cloud SDK uses the right project for all the commands in this notebook.\n",
         "\n",
-        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
+        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1460fd744366"
+      },
+      "source": [
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
       ]
     },
     {
@@ -266,7 +313,7 @@
         "#### 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",
+        "throughout the rest of this notebook.  Below are regions supported for Vertex AI. It is recommended that you choose the region closest to you.\n",
         "\n",
         "- Americas: `us-central1`\n",
         "- Europe: `europe-west4`\n",
@@ -274,7 +321,7 @@
         "\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)"
+        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
       ]
     },
     {
@@ -285,7 +332,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -294,9 +344,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -307,36 +357,52 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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": "gcp_authenticate"
+        "id": "32e1cd21a5d5"
       },
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
+        "authenticated. \n",
         "\n",
-        "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
+        "\n",
+        "**If you are using Colab**, run the cell below and follow the instructions\n",
+        "when prompted to authenticate your account via oAuth.\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",
+        "1. In the Cloud Console, go to the [**Create service account key**\n",
+        "   page](https://console.cloud.google.com/apis/credentials/serviceaccountkey).\n",
         "\n",
-        "**Click Create service account**.\n",
+        "2. Click **Create service account**.\n",
         "\n",
-        "In the **Service account name** field, enter a name, and click **Create**.\n",
+        "3. In the **Service account name** field, enter a name, and\n",
+        "   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",
+        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
+        "into the filter box, and select\n",
+        "   **Vertex AI 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",
+        "5. Click *Create*. A JSON file that contains your key downloads to your\n",
+        "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."
+        "6. Enter the path to your service account key as the\n",
+        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
       ]
     },
     {
@@ -355,8 +421,11 @@
         "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 on Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\n",
         "    if \"google.colab\" in sys.modules:\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
@@ -379,7 +448,7 @@
         "\n",
         "**The following steps are required, regardless of your notebook environment.**\n",
         "\n",
-        "When you initialize the Vertex SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
+        "When you initialize the Vertex AI SDK for Python, you specify a Cloud Storage staging bucket. The staging bucket is where all the data associated with your dataset and model resources are retained across sessions.\n",
         "\n",
         "Set the name of your Cloud Storage bucket below. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization."
       ]
@@ -392,7 +461,8 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -403,8 +473,9 @@
       },
       "outputs": [],
       "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -424,7 +495,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -444,7 +515,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gsutil ls -al $BUCKET_URI"
       ]
     },
     {
@@ -453,9 +524,6 @@
         "id": "setup_vars"
       },
       "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
         "### Import libraries and define constants"
       ]
     },
@@ -467,7 +535,8 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip"
+        "import google.cloud.aiplatform as aip\n",
+        "import pandas as pd"
       ]
     },
     {
@@ -476,9 +545,9 @@
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex SDK for Python\n",
+        "## Initialize Vertex AI SDK for Python\n",
         "\n",
-        "Initialize the Vertex SDK for Python for your project and corresponding bucket."
+        "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
       ]
     },
     {
@@ -594,7 +663,7 @@
       "outputs": [],
       "source": [
         "dataset = aip.TabularDataset.create(\n",
-        "    display_name=\"Bank Marketing\" + \"_\" + TIMESTAMP, gcs_source=[IMPORT_FILE]\n",
+        "    display_name=\"Bank Marketing\" + \"_\" + UUID, gcs_source=[IMPORT_FILE]\n",
         ")\n",
         "\n",
         "print(dataset.resource_name)"
@@ -678,13 +747,13 @@
       },
       "outputs": [],
       "source": [
-        "dag = aip.AutoMLTabularTrainingJob(\n",
-        "    display_name=\"bank_\" + TIMESTAMP,\n",
+        "job = aip.AutoMLTabularTrainingJob(\n",
+        "    display_name=\"bank_\" + UUID,\n",
         "    optimization_prediction_type=\"classification\",\n",
         "    optimization_objective=\"minimize-log-loss\",\n",
         ")\n",
         "\n",
-        "print(dag)"
+        "print(job)"
       ]
     },
     {
@@ -730,9 +799,9 @@
       },
       "outputs": [],
       "source": [
-        "model = dag.run(\n",
+        "model = job.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"bank_\" + TIMESTAMP,\n",
+        "    model_display_name=\"bank_\" + UUID,\n",
         "    training_fraction_split=0.6,\n",
         "    validation_fraction_split=0.2,\n",
         "    test_fraction_split=0.2,\n",
@@ -808,7 +877,7 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=bank_\" + TIMESTAMP)\n",
+        "models = aip.Model.list(filter=\"display_name=bank_\" + UUID)\n",
         "\n",
         "# Get a reference to the Model Service client\n",
         "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -887,7 +956,7 @@
       "source": [
         "### Make test items\n",
         "\n",
-        "You will use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "You use synthetic data as a test data items. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
       ]
     },
     {
@@ -898,7 +967,7 @@
       "source": [
         "### Make the batch input file\n",
         "\n",
-        "Now make a batch input file, which you will store in your local Cloud Storage bucket. Unlike image, video and text, the batch input file for tabular is only supported for CSV. For CSV file, you make:\n",
+        "Now make a batch input file, which you store in your local Cloud Storage bucket. Unlike image, video and text, the batch input file for tabular is only supported for CSV. For CSV file, you make:\n",
         "\n",
         "- The first line is the heading with the feature (fields) heading names.\n",
         "- Each remaining line is a separate prediction request with the corresponding feature values.\n",
@@ -922,7 +991,7 @@
         "\n",
         "! cut -d, -f1-16 tmp.csv > batch.csv\n",
         "\n",
-        "gcs_input_uri = BUCKET_NAME + \"/test.csv\"\n",
+        "gcs_input_uri = BUCKET_URI + \"/test.csv\"\n",
         "\n",
         "! gsutil cp batch.csv $gcs_input_uri"
       ]
@@ -954,9 +1023,9 @@
       "outputs": [],
       "source": [
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"bank_\" + TIMESTAMP,\n",
+        "    job_display_name=\"bank_\" + UUID,\n",
         "    gcs_source=gcs_input_uri,\n",
-        "    gcs_destination_prefix=BUCKET_NAME,\n",
+        "    gcs_destination_prefix=BUCKET_URI,\n",
         "    instances_format=\"csv\",\n",
         "    predictions_format=\"csv\",\n",
         "    sync=False,\n",
@@ -1064,21 +1133,20 @@
       },
       "outputs": [],
       "source": [
-        "import tensorflow as tf\n",
-        "\n",
         "bp_iter_outputs = batch_predict_job.iter_outputs()\n",
         "\n",
         "prediction_results = list()\n",
         "for blob in bp_iter_outputs:\n",
-        "    if blob.name.split(\"/\")[-1].startswith(\"prediction\"):\n",
+        "    if blob.name.split(\"/\")[-1].startswith(\"prediction.results\"):\n",
         "        prediction_results.append(blob.name)\n",
         "\n",
-        "tags = list()\n",
         "for prediction_result in prediction_results:\n",
         "    gfile_name = f\"gs://{bp_iter_outputs.bucket.name}/{prediction_result}\"\n",
-        "    with tf.io.gfile.GFile(name=gfile_name, mode=\"r\") as gfile:\n",
-        "        for line in gfile.readlines():\n",
-        "            print(line)"
+        "    df = pd.read_csv(gfile_name)\n",
+        "    print(f\"File name: {gfile_name}\")\n",
+        "    print(\"Prediction: \\n\\n\\n\\n\")\n",
+        "    print(df)\n",
+        "    print(\"\\n\\n\\n\")"
       ]
     },
     {
@@ -1089,11 +1157,20 @@
       "source": [
         "*Example Output:*\n",
         "\n",
-        "    Age,Job,MaritalStatus,Education,Default,Balance,Housing,Loan,Contact,Day,Month,Duration,Campaign,PDays,Previous,POutcome,Deposit_1_scores,Deposit_2_scores\n",
+        "    File name: gs://vertex-ai-devaip-5j22pmou/prediction-bank_5j22pmou-2022_08_24T01_05_46_028Z/prediction.results-00005-of-00008.csv\n",
+        "Prediction: \n",
         "\n",
-        "    72,retired,married,secondary,no,5715,no,no,cellular,17,nov,1127,5,184,3,success,0.4721628427505493,0.5278371572494507\n",
         "\n",
-        "    57,blue-collar,married,secondary,no,668,no,no,telephone,17,nov,508,4,-1,0,unknown,0.9005520343780518,0.09944798052310944"
+        "\n",
+        "\n",
+        "    Age          Job MaritalStatus  Education Default  Balance Housing Loan  \\\n",
+        "0    57  blue-collar       married  secondary      no      668      no   no   \n",
+        "\n",
+        "     Contact  Day Month  Duration  Campaign  PDays  Previous POutcome  \\\n",
+        "0  telephone   17   nov       508         4     -1         0  unknown   \n",
+        "\n",
+        "    Deposit_1_scores  Deposit_2_scores  \n",
+        "0          0.847498          0.152502  "
       ]
     },
     {
@@ -1173,7 +1250,7 @@
       "source": [
         "### Make test item\n",
         "\n",
-        "You will use synthetic data as a test data item. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
+        "You use synthetic data as a test data item. Don't be concerned that we are using synthetic data -- we just want to demonstrate how to make a prediction."
       ]
     },
     {
@@ -1293,13 +1370,10 @@
         "Otherwise, you can delete the individual resources you created in this tutorial:\n",
         "\n",
         "- Dataset\n",
-        "- Pipeline\n",
         "- Model\n",
         "- Endpoint\n",
         "- AutoML Training Job\n",
         "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
         "- Cloud Storage Bucket"
       ]
     },
@@ -1311,60 +1385,24 @@
       },
       "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",
-        "    try:\n",
-        "        if \"dataset\" in globals():\n",
-        "            dataset.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the model using the Vertex model object\n",
+        "model.delete()\n",
         "\n",
-        "    # Delete the model using the Vertex model object\n",
-        "    try:\n",
-        "        if \"model\" in globals():\n",
-        "            model.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.delete()\n",
         "\n",
-        "    # Delete the endpoint using the Vertex endpoint object\n",
-        "    try:\n",
-        "        if \"endpoint\" in globals():\n",
-        "            endpoint.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the AutoML or Pipeline trainig job\n",
+        "job.delete()\n",
         "\n",
-        "    # Delete the AutoML or Pipeline trainig job\n",
-        "    try:\n",
-        "        if \"dag\" in globals():\n",
-        "            dag.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the batch prediction job using the Vertex batch prediction object\n",
+        "batch_predict_job.delete()\n",
         "\n",
-        "    # Delete the custom trainig job\n",
-        "    try:\n",
-        "        if \"job\" in globals():\n",
-        "            job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the batch prediction job using the Vertex batch prediction object\n",
-        "    try:\n",
-        "        if \"batch_predict_job\" in globals():\n",
-        "            batch_predict_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
-        "    try:\n",
-        "        if \"hpt_job\" in globals():\n",
-        "            hpt_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    if \"BUCKET_NAME\" in globals():\n",
-        "        ! gsutil rm -r $BUCKET_NAME"
+        "delete_bucket = False\n",
+        "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
+        "    ! gsutil -m rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.ipynb

@@ -32,18 +32,26 @@
         "# Vertex AI: Vertex AI Migration: AutoML Image Object Detection\n",
         "\n",
         "<table align=\"left\">\n",
+        "\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/UJ5%20Vertex%20SDK%20AutoML%20Image%20Object%20Detection.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.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/ai-platform-samples/blob/master/vertex-ai-samples/tree/master/notebooks/official/migration/UJ5%20Vertex%20SDK%20AutoML%20Image%20Object%20Detection.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.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://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/migration/UJ5 Vertex SDK AutoML Image Object Detection.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>\n",
+        "\n",
         "<br/><br/><br/>"
       ]
     },
@@ -119,7 +127,7 @@
       "source": [
         "## Installation\n",
         "\n",
-        "Install the latest version of Vertex SDK for Python."
+        "Install the latest version of Vertex AI SDK for Python."
       ]
     },
     {
@@ -138,7 +146,7 @@
         "else:\n",
         "    USER_FLAG = \"\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG"
+        "! pip3 install --upgrade google-cloud-aiplatform $USER_FLAG -q"
       ]
     },
     {
@@ -150,17 +158,6 @@
         "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"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -169,8 +166,9 @@
       },
       "outputs": [],
       "source": [
-        "if os.getenv(\"IS_TESTING\"):\n",
-        "    ! pip3 install --upgrade tensorflow $USER_FLAG"
+        "! pip3 install -U google-cloud-storage $USER_FLAG -q\n",
+        "\n",
+        "! pip3 install --upgrade tensorflow $USER_FLAG -q"
       ]
     },
     {
@@ -297,7 +295,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -306,9 +307,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -319,9 +320,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -332,7 +340,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already authenticated. Skip this step.\n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions when prompted to authenticate your account via oAuth.\n",
         "\n",
@@ -367,8 +375,11 @@
         "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 on Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\n",
         "    if \"google.colab\" in sys.modules:\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
@@ -404,7 +415,8 @@
       },
       "outputs": [],
       "source": [
-        "BUCKET_NAME = \"gs://[your-bucket-name]\"  # @param {type:\"string\"}"
+        "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -415,8 +427,9 @@
       },
       "outputs": [],
       "source": [
-        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"gs://[your-bucket-name]\":\n",
-        "    BUCKET_NAME = \"gs://\" + PROJECT_ID + \"aip-\" + TIMESTAMP"
+        "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -436,7 +449,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil mb -l $REGION $BUCKET_NAME"
+        "! gsutil mb -l $REGION -p $PROJECT_ID $BUCKET_URI"
       ]
     },
     {
@@ -456,7 +469,7 @@
       },
       "outputs": [],
       "source": [
-        "! gsutil ls -al $BUCKET_NAME"
+        "! gsutil ls -al $BUCKET_URI"
       ]
     },
     {
@@ -501,7 +514,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_NAME)"
+        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -603,7 +616,7 @@
       "outputs": [],
       "source": [
         "dataset = aip.ImageDataset.create(\n",
-        "    display_name=\"Salads\" + \"_\" + TIMESTAMP,\n",
+        "    display_name=\"Salads\" + \"_\" + UUID,\n",
         "    gcs_source=[IMPORT_FILE],\n",
         "    import_schema_uri=aip.schema.dataset.ioformat.image.bounding_box,\n",
         ")\n",
@@ -688,7 +701,7 @@
       "outputs": [],
       "source": [
         "dag = aip.AutoMLImageTrainingJob(\n",
-        "    display_name=\"salads_\" + TIMESTAMP,\n",
+        "    display_name=\"salads_\" + UUID,\n",
         "    prediction_type=\"object_detection\",\n",
         "    multi_label=False,\n",
         "    model_type=\"CLOUD\",\n",
@@ -742,7 +755,7 @@
       "source": [
         "model = dag.run(\n",
         "    dataset=dataset,\n",
-        "    model_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    model_display_name=\"salads_\" + UUID,\n",
         "    training_fraction_split=0.8,\n",
         "    validation_fraction_split=0.1,\n",
         "    test_fraction_split=0.1,\n",
@@ -815,7 +828,7 @@
       "outputs": [],
       "source": [
         "# Get model resource ID\n",
-        "models = aip.Model.list(filter=\"display_name=salads_\" + TIMESTAMP)\n",
+        "models = aip.Model.list(filter=\"display_name=salads_\" + UUID)\n",
         "\n",
         "# Get a reference to the Model Service client\n",
         "client_options = {\"api_endpoint\": f\"{REGION}-aiplatform.googleapis.com\"}\n",
@@ -945,11 +958,11 @@
         "file_1 = test_item_1.split(\"/\")[-1]\n",
         "file_2 = test_item_2.split(\"/\")[-1]\n",
         "\n",
-        "! gsutil cp $test_item_1 $BUCKET_NAME/$file_1\n",
-        "! gsutil cp $test_item_2 $BUCKET_NAME/$file_2\n",
+        "! gsutil cp $test_item_1 $BUCKET_URI/$file_1\n",
+        "! gsutil cp $test_item_2 $BUCKET_URI/$file_2\n",
         "\n",
-        "test_item_1 = BUCKET_NAME + \"/\" + file_1\n",
-        "test_item_2 = BUCKET_NAME + \"/\" + file_2"
+        "test_item_1 = BUCKET_URI + \"/\" + file_1\n",
+        "test_item_2 = BUCKET_URI + \"/\" + file_2"
       ]
     },
     {
@@ -982,7 +995,7 @@
         "\n",
         "import tensorflow as tf\n",
         "\n",
-        "gcs_input_uri = BUCKET_NAME + \"/test.jsonl\"\n",
+        "gcs_input_uri = BUCKET_URI + \"/test.jsonl\"\n",
         "with tf.io.gfile.GFile(gcs_input_uri, \"w\") as f:\n",
         "    data = {\"content\": test_item_1, \"mime_type\": \"image/jpeg\"}\n",
         "    f.write(json.dumps(data) + \"\\n\")\n",
@@ -1018,9 +1031,9 @@
       "outputs": [],
       "source": [
         "batch_predict_job = model.batch_predict(\n",
-        "    job_display_name=\"salads_\" + TIMESTAMP,\n",
+        "    job_display_name=\"salads_\" + UUID,\n",
         "    gcs_source=gcs_input_uri,\n",
-        "    gcs_destination_prefix=BUCKET_NAME,\n",
+        "    gcs_destination_prefix=BUCKET_URI,\n",
         "    sync=False,\n",
         ")\n",
         "\n",
@@ -1378,60 +1391,25 @@
       },
       "outputs": [],
       "source": [
-        "delete_all = True\n",
+        "# Delete the dataset using the Vertex dataset object\n",
         "\n",
-        "if delete_all:\n",
-        "    # Delete the dataset using the Vertex dataset object\n",
-        "    try:\n",
-        "        if \"dataset\" in globals():\n",
-        "            dataset.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "dataset.delete()\n",
         "\n",
-        "    # Delete the model using the Vertex model object\n",
-        "    try:\n",
-        "        if \"model\" in globals():\n",
-        "            model.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the model using the Vertex model object\n",
+        "model.delete()\n",
         "\n",
-        "    # Delete the endpoint using the Vertex endpoint object\n",
-        "    try:\n",
-        "        if \"endpoint\" in globals():\n",
-        "            endpoint.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.delete()\n",
         "\n",
-        "    # Delete the AutoML or Pipeline trainig job\n",
-        "    try:\n",
-        "        if \"dag\" in globals():\n",
-        "            dag.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the AutoML or Pipeline trainig job\n",
         "\n",
-        "    # Delete the custom trainig job\n",
-        "    try:\n",
-        "        if \"job\" in globals():\n",
-        "            job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "dag.delete()\n",
         "\n",
-        "    # Delete the batch prediction job using the Vertex batch prediction object\n",
-        "    try:\n",
-        "        if \"batch_predict_job\" in globals():\n",
-        "            batch_predict_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
+        "# Delete the batch prediction job using the Vertex batch prediction object\n",
+        "batch_predict_job.delete()\n",
         "\n",
-        "    # Delete the hyperparameter tuning job using the Vertex hyperparameter tuning object\n",
-        "    try:\n",
-        "        if \"hpt_job\" in globals():\n",
-        "            hpt_job.delete()\n",
-        "    except Exception as e:\n",
-        "        print(e)\n",
-        "\n",
-        "    if \"BUCKET_NAME\" in globals():\n",
-        "        ! gsutil rm -r $BUCKET_NAME"
+        "if \"BUCKET_NAME\" in globals():\n",
+        "    ! gsutil rm -r $BUCKET_URI"
       ]
     }
   ],

notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb

@@ -8,7 +8,7 @@
       },
       "outputs": [],
       "source": [
-        "# Copyright 2022 Google LLC\n",
+        "# Copyright 2021 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",
@@ -29,23 +29,21 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Vertex AI: Track parameters and metrics for custom training jobs\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/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.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/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/master/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.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://github.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb\">\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-custom-jobs.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",
@@ -56,39 +54,48 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "tvgnzT1CKxrO"
+        "id": "j9gUDU_3vV9d"
       },
       "source": [
-        "## Overview\n",
-        "\n",
-        "This notebook demonstrates how to track metrics and parameters for `Vertex AI` custom training jobs, and how to perform detailed analysis using this data."
+        "# Vertex AI: Track parameters and metrics for custom training jobs"
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "37147bd9c3c4"
+        "id": "2e0464050974"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates how to track metrics and parameters for Vertex AI custom training jobs, and how to perform detailed analysis using this data."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b95ab729fccd"
       },
       "source": [
         "### Objective\n",
         "\n",
-        "In this notebook, you learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.\n",
+        "In this notebook, you will learn how to use Vertex AI SDK for Python to:\n",
         "\n",
-        "This tutorial uses the following Google Cloud ML services:\n",
-        "\n",
-        "- `Vertex ML Metadata`\n",
-        "- `Vertex AI Experiments`\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "- Vertex AI Dataset\n",
+        "- Vertex AI Model\n",
+        "- Vertex AI Endpoint\n",
+        "- Vertex AI Custom Training Job\n",
         "\n",
         "The steps performed include:\n",
-        "\n",
-        "- Track parameters and metrics for a `Vertex AI` custom trained model.\n",
+        "- Track training parameters and prediction metrics for a custom training job.\n",
         "- Extract and perform analysis for all parameters and metrics within an Experiment."
       ]
     },
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "96cb18467417"
+        "id": "9fd87cf689bf"
       },
       "source": [
         "### Dataset\n",
@@ -99,7 +106,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "c831245dc1d5"
+        "id": "tvgnzT1CKxrO"
       },
       "source": [
         "### Costs \n",
@@ -181,14 +188,14 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "IaYsrh0Tc17L"
+        "id": "qblyW_dcyOQA"
       },
       "outputs": [],
       "source": [
         "import os\n",
         "\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_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
         "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
         "    \"/opt/deeplearning/metadata/env_version\"\n",
         ")\n",
@@ -198,9 +205,10 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "! pip3 install -U tensorflow $USER_FLAG -q\n",
-        "! pip3 install scikit-learn {USER_FLAG} -q"
+        "\n",
+        "! pip3 install -U tensorflow $USER_FLAG\n",
+        "! python3 -m pip3 install {USER_FLAG} google-cloud-aiplatform --upgrade\n",
+        "! pip3 install scikit-learn {USER_FLAG}"
       ]
     },
     {
@@ -285,7 +293,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "oM1iC_MfAts1"
+        "id": "cde8e0876d62"
       },
       "outputs": [],
       "source": [
@@ -296,11 +304,11 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "riG_qUokg0XZ"
+        "id": "oM1iC_MfAts1"
       },
       "outputs": [],
       "source": [
-        "if PROJECT_ID == \"[your-project-id]\" or PROJECT_ID == \"\" or PROJECT_ID is None:\n",
+        "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",
@@ -321,7 +329,7 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "region"
+        "id": "47bc07d4231b"
       },
       "source": [
         "#### Region\n",
@@ -335,14 +343,14 @@
         "\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)"
+        "Learn more about [Vertex AI regions](https://cloud.google.com/vertex-ai/docs/general/locations)."
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "region"
+        "id": "959545da671a"
       },
       "outputs": [],
       "source": [
@@ -358,9 +366,9 @@
         "id": "06571eb4063b"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -371,9 +379,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\n",
+        "# Generate a uuid of length 8\n",
+        "def generate_uuid():\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=8))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -385,7 +400,7 @@
         "### Authenticate your Google Cloud account\n",
         "\n",
         "**If you are using Vertex AI Workbench**, your environment is already\n",
-        "authenticated. "
+        "authenticated. Skip this step."
       ]
     },
     {
@@ -435,7 +450,6 @@
         "# requests.\n",
         "\n",
         "# If on Google Cloud Notebooks, then don't execute this code\n",
-        "IS_COLAB = \"google.colab\" in sys.modules\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",
@@ -460,7 +474,7 @@
         "**The following steps are required, regardless of your notebook environment.**\n",
         "\n",
         "\n",
-        "When you submit a training job using the Cloud SDK, you upload a Python package\n",
+        "When you submit a training job using the Vertex AI SDK, you upload a Python package\n",
         "containing your training code to a Cloud Storage bucket. Vertex AI runs\n",
         "the code from this package. In this tutorial, Vertex AI also saves the\n",
         "trained model that results from your job in the same bucket. Using this model artifact, you can then\n",
@@ -492,8 +506,8 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
-        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -615,7 +629,7 @@
       "outputs": [],
       "source": [
         "if EXPERIMENT_NAME == \"\" or EXPERIMENT_NAME is None:\n",
-        "    EXPERIMENT_NAME = \"my-experiment-\" + TIMESTAMP"
+        "    EXPERIMENT_NAME = \"my-experiment-\" + UUID"
       ]
     },
     {
@@ -655,10 +669,10 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "9nokDKBAxwV8"
+        "id": "f8fd397cc4f6"
       },
       "source": [
-        "This example uses the Abalone Dataset. For more information about this dataset please visit: https://archive.ics.uci.edu/ml/datasets/abalone"
+        "### Download the Dataset to Cloud Storage"
       ]
     },
     {
@@ -681,9 +695,9 @@
         "id": "35QVNhACqcTJ"
       },
       "source": [
-        "### Create a Vertex AI Dataset from a CSV\n",
+        "### Create a Vertex AI Tabular dataset from CSV data\n",
         "\n",
-        "A Vertex AI Dataset can be used to create an AutoML model or a custom model. "
+        "A Vertex AI dataset can be used to create an AutoML model or a custom model. "
       ]
     },
     {
@@ -696,7 +710,7 @@
       "source": [
         "ds = aiplatform.TabularDataset.create(display_name=\"abalone\", gcs_source=[gcs_csv_path])\n",
         "\n",
-        "print(ds.resource_name)"
+        "ds.resource_name"
       ]
     },
     {
@@ -707,7 +721,7 @@
       "source": [
         "### Write the training script\n",
         "\n",
-        "Run the following cell to create the training script that is used in the sample custom training job."
+        "Next, you create the training script that is used in the sample custom training job."
       ]
     },
     {
@@ -735,9 +749,6 @@
         "                    default=64, type=int,\n",
         "                    help='Number of unit for first layer.')\n",
         "args = parser.parse_args()\n",
-        "# uncomment and bump up replica_count for distributed training\n",
-        "# strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy()\n",
-        "# tf.distribute.experimental_set_strategy(strategy)\n",
         "\n",
         "col_names = [\"Length\", \"Diameter\", \"Height\", \"Whole weight\", \"Shucked weight\", \"Viscera weight\", \"Shell weight\", \"Age\"]\n",
         "target = \"Age\"\n",
@@ -771,7 +782,7 @@
         "id": "Yp2clkOJSDhR"
       },
       "source": [
-        "### Launch a custom training job and track its trainig parameters on Vertex AI ML Metadata"
+        "### Launch a custom training job and track its trainig parameters on Vertex ML Metadata"
       ]
     },
     {
@@ -797,11 +808,7 @@
         "id": "k_QorXXztzPH"
       },
       "source": [
-        "Start a new experiment run to track training parameters and start the training job. \n",
-        "\n",
-        "Prior to executing the training job, you call the `start_run()` method to initialize the start of the experiment, and then use the `log_params()` to log the parameters used in the experiment.\n",
-        "\n",
-        "*Note:* This operation will take around 10 mins."
+        "Start a new experiment run to track training parameters and start the training job. Note that this operation will take around 10 mins."
       ]
     },
     {
@@ -830,7 +837,7 @@
         "id": "5vhDsMJNqcTW"
       },
       "source": [
-        "### Deploy Model and calculate prediction metrics"
+        "### Deploy model and calculate prediction metrics"
       ]
     },
     {
@@ -839,7 +846,7 @@
         "id": "O-uCOL3Naap4"
       },
       "source": [
-        "Deploy model to Google Cloud. This operation may take a few minutes."
+        "Next, deploy your Vertex AI Model resource to a Vertex AI Endpoint resource. This operation will take 10-20 mins."
       ]
     },
     {
@@ -859,7 +866,7 @@
         "id": "JY-5skFhasWs"
       },
       "source": [
-        "Once model is deployed, perform online prediction using the `abalone_test` dataset and calculate prediction metrics."
+        "### Prediction dataset preparation and online prediction"
       ]
     },
     {
@@ -868,6 +875,8 @@
         "id": "saw50bqwa-dR"
       },
       "source": [
+        "Once model is deployed, perform online prediction using the `abalone_test` dataset and calculate prediction metrics.\n",
+        "\n",
         "Prepare the prediction dataset."
       ]
     },
@@ -920,7 +929,7 @@
         "id": "_HphZ38obJeB"
       },
       "source": [
-        "### Perform online prediction"
+        "Perform online prediction."
       ]
     },
     {
@@ -932,7 +941,7 @@
       "outputs": [],
       "source": [
         "prediction = endpoint.predict(test_dataset.tolist())\n",
-        "print(prediction)"
+        "prediction"
       ]
     },
     {
@@ -941,11 +950,7 @@
         "id": "TDKiv_O7bNwE"
       },
       "source": [
-        "### Calculate and track prediction evaluation metrics.\n",
-        "\n",
-        "Next, log the evaluation metrics for your experiment.\n",
-        "\n",
-        "Once the experiment is completed, you call the `end_run()` method to indicate the end of tracking for the experiment."
+        "Calculate and track prediction evaluation metrics."
       ]
     },
     {
@@ -959,9 +964,7 @@
         "mse = mean_squared_error(test_labels, prediction.predictions)\n",
         "mae = mean_absolute_error(test_labels, prediction.predictions)\n",
         "\n",
-        "aiplatform.log_metrics({\"mse\": mse, \"mae\": mae})\n",
-        "\n",
-        "aiplatform.end_run()"
+        "aiplatform.log_metrics({\"mse\": mse, \"mae\": mae})"
       ]
     },
     {

notebooks/official/model-registry/bqml-vertexai-model-registry.ipynb

@@ -0,0 +1,921 @@
+{
+  "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": [
+        "<table align=\"left\">\n",
+        "\n",
+        "  <td>\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model-registry/bqml-vertexai-model-registry.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/model-registry/bqml-vertexai-model-registry.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/model-registry/bqml-vertexai-model-registry.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>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tvgnzT1CKxrO"
+      },
+      "source": [
+        "# Deploy BiqQuery ML Model on Vertex AI Model Registry and Make Predictions\n",
+        "\n",
+        "## Overview\n",
+        "\n",
+        "This tutorial demonstrates how to train a model with BigQuery ML and upload it on Vertex AI model registry, then make batch predictions.\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "132a9ee68ba6"
+      },
+      "source": [
+        "### Objective\n",
+        "\n",
+        "In this tutorial, you learn how to use `Vertex AI Model Registry` with `BigQuery ML` and make batch predictions:\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- `Vertex AI Model Registry`\n",
+        "- `Vertex AI Model` resources \n",
+        "- `Vertex AI Endpoint` resources\n",
+        "- `Vertex AI Prediction`\n",
+        "- `BigQuery ML`\n",
+        "\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Train a model with `BQML`\n",
+        "- Upload the model to `Vertex AI Model Registry` \n",
+        "- Create a `Vertex AI Endpoint` resource\n",
+        "- Deploy the `Model` resource to the `Endpoint` resource\n",
+        "- Make `prediction` requests to the model endpoint\n",
+        "- Run `batch prediction` job on the `Model` resource \n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2de0477b10ce"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the Penguins dataset from <a href=\"https://cloud.google.com/bigquery/public-data\" target=\"_blank\">BigQuery public datasets</a>. This version of the dataset is used to predict the species of penguins from the available features like culmen-length, flipper-depth etc."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "76330e07673b"
+      },
+      "source": [
+        "### Costs \n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "* BigQuery ML\n",
+        "\n",
+        "Learn about <a href=\"https://cloud.google.com/vertex-ai/pricing\" target=\"_blank\">Vertex AI\n",
+        "pricing</a> and <a href=\"https://cloud.google.com/bigquery/pricing\" target=\"_blank\">BigQuery pricing</a>, and use the <a href=\"https://cloud.google.com/products/calculator/\" target=\"_blank\">Pricing\n",
+        "Calculator</a>\n",
+        "to generate a cost estimate based on your projected usage."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ze4-nDLfK4pw"
+      },
+      "source": [
+        "### Set up your local development environment\n",
+        "\n",
+        "**If you are using Colab or Vertex AI Workbench Notebooks**, your environment already meets\n",
+        "all the requirements to run this notebook. You can skip this step."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gCuSR8GkAgzl"
+      },
+      "source": [
+        "**Otherwise**, make sure your environment meets this notebook's requirements.\n",
+        "You need the following:\n",
+        "\n",
+        "* The Google Cloud SDK\n",
+        "* Git\n",
+        "* Python 3\n",
+        "* virtualenv\n",
+        "* Jupyter notebook running in a virtual environment with Python 3\n",
+        "\n",
+        "The Google Cloud guide to <a href=\"https://cloud.google.com/python/setup\" target=\"_blank\">Setting up a Python development\n",
+        "environment</a> and the <a href=\"https://jupyter.org/install\" target=\"_blank\">Jupyter\n",
+        "installation guide</a> provide detailed instructions\n",
+        "for meeting these requirements. The following steps provide a condensed set of\n",
+        "instructions:\n",
+        "\n",
+        "1. <a href=\"https://cloud.google.com/sdk/docs/\" target=\"_blank\">Install and initialize the Cloud SDK.</a>\n",
+        "\n",
+        "1. <a href=\"https://cloud.google.com/python/setup#installing_python\" target=\"_blank\">Install Python 3.</a>\n",
+        "\n",
+        "1. <a href=\"https://cloud.google.com/python/setup#installing_and_using_virtualenv\" target=\"_blank\">Install\n",
+        "   virtualenv</a>\n",
+        "   and create a virtual environment that uses Python 3. Activate the virtual environment.\n",
+        "\n",
+        "1. To install Jupyter, run `pip3 install jupyter` on the\n",
+        "command-line in a terminal shell.\n",
+        "\n",
+        "1. To launch Jupyter, run `jupyter notebook` on the command-line in a terminal shell.\n",
+        "\n",
+        "1. Open this notebook in the Jupyter Notebook Dashboard."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i7EUnXsZhAGF"
+      },
+      "source": [
+        "### Install additional packages\n",
+        "\n",
+        "Install the following packages required to execute this notebook. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2b4ef9b72d43"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "\n",
+        "# The Vertex AI Workbench Notebook product has specific requirements\n",
+        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
+        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\n",
+        "\n",
+        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
+        "USER_FLAG = \"\"\n",
+        "if IS_WORKBENCH_NOTEBOOK:\n",
+        "    USER_FLAG = \"--user\"\n",
+        "\n",
+        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
+        "! pip3 install --upgrade google-cloud-bigquery {USER_FLAG} -q"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hhq5zEbGg0XX"
+      },
+      "source": [
+        "### Restart the kernel\n",
+        "\n",
+        "After you install the additional packages, you need to restart the notebook kernel so it can find the packages."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "EzrelQZ22IZj"
+      },
+      "outputs": [],
+      "source": [
+        "# Automatically restart kernel after installs\n",
+        "import os\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)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lWEdiXsJg0XY"
+      },
+      "source": [
+        "## Before you begin"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BF1j6f9HApxa"
+      },
+      "source": [
+        "### Set up your Google Cloud project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. <a href=\"https://console.cloud.google.com/cloud-resource-manager\" target=\"_blank\">Select or create a Google Cloud project</a>. When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "1. <a href=\"https://cloud.google.com/billing/docs/how-to/modify-project\" target=\"_blank\">Make sure that billing is enabled for your project</a>.\n",
+        "\n",
+        "1. <a href=\"https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com,bigquery\" target=\"_blank\">Enable the Vertex AI and BigQuery APIs</a>. \n",
+        "\n",
+        "1. If you are running this notebook locally, you will need to install the <a href=\"https://cloud.google.com/sdk\" target=\"_blank\">Cloud SDK</a>.\n",
+        "\n",
+        "1. Enter your project ID in the cell below. Then run the cell to make sure the\n",
+        "Cloud SDK uses the right project for all the commands in this notebook.\n",
+        "\n",
+        "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WReHDGG5g0XY"
+      },
+      "source": [
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, you can get your project ID using `gcloud`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "o1AuQDpf_hS-"
+      },
+      "outputs": [],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "RYbBU1jXAETD"
+      },
+      "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"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "region"
+      },
+      "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 might not be able to 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 <a href=\"https://cloud.google.com/vertex-ai/docs/general/locations\" target=\"_blank\">Vertex AI regions</a>."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "vO3W8YdN2LuA"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "29e912d1b106"
+      },
+      "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": 2,
+      "metadata": {
+        "id": "c704897922c0"
+      },
+      "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": "dr--iN2kAylZ"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
+        "authenticated. Skip this step."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBCra4QMA2wR"
+      },
+      "source": [
+        "**If you are using Colab**, run the cell below and follow the instructions\n",
+        "when prompted to authenticate your account via oAuth.\n",
+        "\n",
+        "**Otherwise**, follow these steps:\n",
+        "\n",
+        "1. In the Cloud Console, go to the <a href=\"https://console.cloud.google.com/apis/credentials/serviceaccountkey\" target=\"_blank\">Create service account key page</a>.\n",
+        "\n",
+        "2. Click **Create service account**.\n",
+        "\n",
+        "3. In the **Service account name** field, enter a name, and\n",
+        "   click **Create**.\n",
+        "\n",
+        "4. In the **Grant this service account access to project** section, click the **Role** drop-down list. Type \"Vertex AI\"\n",
+        "into the filter box, and select\n",
+        "   **Vertex AI Administrator**. Type \"Storage Object Admin\" into the filter box, and select **Storage Object Admin**.\n",
+        "\n",
+        "5. Click *Create*. A JSON file that contains your key downloads to your\n",
+        "local environment.\n",
+        "\n",
+        "6. Enter the path to your service account key as the\n",
+        "`GOOGLE_APPLICATION_CREDENTIALS` variable in the cell below and run the cell."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "PyQmSRbKA8r-"
+      },
+      "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 Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\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 ''"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "XoEqT2Y4DJmf"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "pRUOFELefqf1"
+      },
+      "outputs": [],
+      "source": [
+        "import google.cloud.aiplatform as aiplatform\n",
+        "from google.cloud import bigquery"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "init_aip:mbsdk,all"
+      },
+      "source": [
+        "### Initialize Vertex AI and BigQuery SDKs for Python\n",
+        "\n",
+        "Initialize the Vertex AI and Big Query SDKs for Python for your project and corresponding bucket."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "BgaYKz2-2LuC"
+      },
+      "outputs": [],
+      "source": [
+        "aiplatform.init(project=PROJECT_ID, location=REGION)\n",
+        "bqclient = bigquery.Client(project=PROJECT_ID)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lUEtpimzL17Z"
+      },
+      "source": [
+        "## BigQuery ML introduction\n",
+        "\n",
+        "BigQuery ML (BQML) provides the capability to train ML tabular models, such as classification and regression, in BigQuery using SQL syntax.\n",
+        "\n",
+        "Learn more about <a href=\"https://cloud.google.com/bigquery-ml/docs\" target=\"_blank\">BigQuery ML documentation</a>."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "30adf1b74bf9"
+      },
+      "source": [
+        "### BigQuery table used for training"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3O7qlOGWNEU4"
+      },
+      "outputs": [],
+      "source": [
+        "# Define BigQuery table to be used for training\n",
+        "\n",
+        "BQ_TABLE = \"bigquery-public-data.ml_datasets.penguins\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PKQD2e0eMg3M"
+      },
+      "source": [
+        "### Create BigQuery dataset resource"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "BnXOpvs2MmzF"
+      },
+      "source": [
+        "First, you create an empty dataset resource in your project."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "luqb-DBiMn-0"
+      },
+      "outputs": [],
+      "source": [
+        "BQ_DATASET_NAME = \"penguins\" + UUID\n",
+        "DATASET_QUERY = f\"\"\"CREATE SCHEMA {BQ_DATASET_NAME}\"\"\"\n",
+        "\n",
+        "job = bqclient.query(DATASET_QUERY)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "59bf85366baf"
+      },
+      "outputs": [],
+      "source": [
+        "job.result()\n",
+        "print(job.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b-_8rZO8NIEb"
+      },
+      "source": [
+        "## Train BigQuery ML model and upload it to Vertex AI Model Registry\n",
+        "Next, you create and train a BQML tabular regression model from the public dataset penguins and store the model in your project `Vertex AI Model Registry` using the `CREATE MODEL` statement. The model configuration is specified in the `OPTIONS` statement as follows:\n",
+        "\n",
+        "- `model_type`: The type and archictecture of tabular model to train, e.g., LOGISTIC_REG.\n",
+        "\n",
+        "- `labels`: The column which are the labels.\n",
+        "\n",
+        "- `model_registry`: To register a BigQuery ML model to Vertex AI Model Registry, you must use `model_registry=\"vertex_ai\"`.\n",
+        "\n",
+        "Learn more about the <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-create\" target=\"_blank\">CREATE MODEL statement</a>.\n",
+        "\n",
+        "Learn more about <a href=\"https://cloud.google.com/bigquery-ml/docs/managing-models-vertex\" target=\"_blank\">Managing BigQuery ML models in the Vertex AI Model Registry</a>."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Q96rlZKRNPjU"
+      },
+      "outputs": [],
+      "source": [
+        "# Write the query to create Big Query ML model\n",
+        "\n",
+        "MODEL_NAME = \"penguins-lr\" + UUID\n",
+        "MODEL_QUERY = f\"\"\"\n",
+        "CREATE OR REPLACE MODEL `{BQ_DATASET_NAME}.{MODEL_NAME}`\n",
+        "OPTIONS(\n",
+        "    model_type='LOGISTIC_REG',\n",
+        "    labels = ['species'],\n",
+        "    model_registry='vertex_ai'\n",
+        "    )\n",
+        "AS\n",
+        "SELECT *\n",
+        "FROM `{BQ_TABLE}`\n",
+        "\"\"\""
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eee158e2a375"
+      },
+      "source": [
+        "Create the BigQuery ML model using the query above and the BigQuery client that you created previously:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LtdieY-BWILs"
+      },
+      "outputs": [],
+      "source": [
+        "# Run the model creation query using BigQuery client\n",
+        "\n",
+        "job = bqclient.query(MODEL_QUERY)\n",
+        "print(f\"Job state: {job.state}\\nJob Error:{job.errors}\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "e4b007777e68"
+      },
+      "source": [
+        "Check the job status:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "Y4C_3hTEXOE7"
+      },
+      "outputs": [],
+      "source": [
+        "job.result()\n",
+        "print(job.state)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5nHvVttrYfQ8"
+      },
+      "source": [
+        "### Find the model in the Vertex Model Registry\n",
+        "\n",
+        "You can use the `Vertex AI Model()` method with `model_name` parameter to find the automatically registered model."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "E08IwUX5YpAG"
+      },
+      "outputs": [],
+      "source": [
+        "model = aiplatform.Model(model_name=MODEL_NAME)\n",
+        "\n",
+        "print(model.gca_resource)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "zgvXXlaZaYw3"
+      },
+      "source": [
+        "## Deploy Vertex AI Model resource to a Vertex AI Endpoint resource\n",
+        "You must deploy a model to an `endpoint` before that model can be used to serve online predictions; deploying a model associates physical resources with the model so it can serve online predictions with low latency. \n",
+        "\n",
+        "Learn more about <a href=\"https://cloud.google.com/vertex-ai/docs/predictions/deploy-model-api#aiplatform_deploy_model_custom_trained_model_sample-python\" target=\"_blank\">Deploy a model using the Vertex AI API</a>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "N27z-_by5gti"
+      },
+      "source": [
+        "### Create a Vertex AI Endpoint resource\n",
+        "\n",
+        "If you are deploying a model to an existing endpoint, you can skip this cell.\n",
+        "\n",
+        "- `display_name`: Display name for the endpoint.\n",
+        "- `project`: The project ID on which you are creating an endpoint.\n",
+        "- `location`: The region where you are using Vertex AI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "XmmyCtW055Ya"
+      },
+      "outputs": [],
+      "source": [
+        "ENDPOINT_DISPLAY_NAME = \"bqml-lr-model-endpoint\" + UUID\n",
+        "\n",
+        "endpoint = aiplatform.Endpoint.create(\n",
+        "    display_name=ENDPOINT_DISPLAY_NAME,\n",
+        "    project=PROJECT_ID,\n",
+        "    location=REGION,\n",
+        ")\n",
+        "\n",
+        "print(endpoint.display_name)\n",
+        "print(endpoint.resource_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xSCIKdFo56YO"
+      },
+      "source": [
+        "### Deploy the Vertex AI Model resource to Vertex AI Endpoint resource"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f7KfDgALE4aD"
+      },
+      "outputs": [],
+      "source": [
+        "DEPLOYED_NAME = \"bqml-lr-penguins\"\n",
+        "\n",
+        "model.deploy(endpoint=endpoint, deployed_model_display_name=DEPLOYED_NAME)\n",
+        "\n",
+        "print(model.display_name)\n",
+        "print(model.resource_name)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "H3K6SVplJ9Mg"
+      },
+      "source": [
+        "## Send prediction request to the Vertex AI Endpoint resource\n",
+        "\n",
+        "Now that your Vertex AI Model resource is deployed to a Vertex AI `Endpoint` resource, you can do online predictions by sending prediction requests to the `Endpoint` resource.\n",
+        "\n",
+        "Learn more about <a href=\"https://cloud.google.com/vertex-ai/docs/predictions/online-predictions-custom-models\" target=\"_blank\">Get online predictions from custom-trained models</a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2j7ioB3VKEtx"
+      },
+      "outputs": [],
+      "source": [
+        "instance = {\n",
+        "    \"island\": \"Dream\",\n",
+        "    \"culmen_length_mm\": 36.6,\n",
+        "    \"culmen_depth_mm\": 18.4,\n",
+        "    \"flipper_length_mm\": 184.0,\n",
+        "    \"body_mass_g\": 3475.0,\n",
+        "    \"sex\": \"FEMALE\",\n",
+        "}\n",
+        "\n",
+        "prediction = endpoint.predict([instance])\n",
+        "print(prediction)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "C39qOaBHZI1G"
+      },
+      "source": [
+        "## Batch Prediction on the BQML model"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UBffk3GyaPY3"
+      },
+      "source": [
+        "Here you request batch predictions directly from the BigQuery ML model; you don't need to deploy the model to an endpoint. For data types that support both batch and online predictions, use batch predictions when you don't require an immediate response and want to process accumulated data by using a single request.\n",
+        "\n",
+        "Learn more abount <a href=\"https://cloud.google.com/bigquery-ml/docs/reference/standard-sql/bigqueryml-syntax-predict\" target=\"_blank\">The ML.PREDICT function</a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "_QxZb19_o6jx"
+      },
+      "outputs": [],
+      "source": [
+        "sql_ml_predict = f\"\"\"SELECT * FROM ML.PREDICT(MODEL `{PROJECT_ID}.{BQ_DATASET_NAME}.{MODEL_NAME}`, \n",
+        "(SELECT\n",
+        "      *\n",
+        "    FROM\n",
+        "      `{BQ_TABLE}` LIMIT 10))\"\"\"\n",
+        "\n",
+        "job = bqclient.query(sql_ml_predict)\n",
+        "prediction_result = job.result().to_arrow().to_pandas()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "TVpsLI5nrVii"
+      },
+      "outputs": [],
+      "source": [
+        "display(prediction_result.head())"
+      ]
+    },
+    {
+      "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 <a href=\"https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects\" target=\"_blank\">delete the Google Cloud\n",
+        "project</a> you used for the tutorial.\n",
+        "\n",
+        "Learn more about <a href=\"https://cloud.google.com/bigquery-ml/docs/managing-models-vertex\" target=\"_blank\">Deleting BigQuery ML models from Vertex AI Model Registry</a>\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": [
+        "# Delete the endpoint using the Vertex endpoint object\n",
+        "endpoint.undeploy_all()\n",
+        "endpoint.delete()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3c5c8dc2f597"
+      },
+      "outputs": [],
+      "source": [
+        "# Delete BigQuery ML model\n",
+        "\n",
+        "delete_query = f\"\"\"DROP MODEL `{PROJECT_ID}.{BQ_DATASET_NAME}.{MODEL_NAME}`\"\"\"\n",
+        "job = bqclient.query(delete_query)\n",
+        "job.result()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "85dfc88f5472"
+      },
+      "outputs": [],
+      "source": [
+        "# Delete the created BigQuery dataset\n",
+        "! bq rm -r -f $PROJECT_ID:$BQ_DATASET_NAME"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "collapsed_sections": [],
+      "name": "bqml-vertexai-model-registry.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/official/model_monitoring/model_monitoring.ipynb

@@ -223,12 +223,23 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
+        "# Don't bother installing tensorflow or explainable_ai_sdk on Colab\n",
+        "extra_pkgs = \"tensorflow explainable_ai_sdk\"\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "  extra_pkgs = \"\"\n",
+        "\n",
         "# Install required packages.\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade google-cloud-aiplatform\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade tensorflow\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade explainable_ai_sdk\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade google-api-python-client google-auth-oauthlib google-auth-httplib2 oauth2client requests\n",
-        "! pip3 install {USER_FLAG} --quiet --upgrade google-cloud-storage==1.32.0"
+        "! pip3 install {USER_FLAG} \\\n",
+        "    google-cloud-aiplatform \\\n",
+        "    explainable_ai_sdk \\\n",
+        "    $extra_pkgs \\\n",
+        "    google-api-python-client \\\n",
+        "    google-auth-oauthlib \\\n",
+        "    google-auth-httplib2 \\\n",
+        "    oauth2client \\\n",
+        "    requests \\\n",
+        "    protobuf==3.20.* \\\n",
+        "    google-cloud-storage==1.32.0 "
       ]
     },
     {
@@ -562,7 +573,7 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "init_aip:mbsdk,region"
+        "id": "wGa5T9eRR8Mz"
       },
       "outputs": [],
       "source": [

notebooks/official/pipelines/automl_tabular_classification_beans.ipynb

@@ -64,22 +64,11 @@
         "\n",
         "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build an AutoML tabular classification workflow on [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines).\n",
         "\n",
-        "You'll build a pipeline that looks like this:\n",
+        "You build a pipeline in this notebook that looks like this:\n",
         "\n",
         "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/beans.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/beans.png\" width=\"95%\"/></a>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:beans,lcn"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the UCI Machine Learning ['Dry beans dataset'](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset), from: KOKLU, M. and OZKAN, I.A., (2020), \"Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.\"In Computers and Electronics in Agriculture, 174, 105507. [DOI](https://doi.org/10.1016/j.compag.2020.105507)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -96,8 +85,8 @@
         "- `Vertex AI Pipelines`\n",
         "- `Google Cloud Pipeline Components`\n",
         "- `Vertex AutoML`\n",
-        "- `Vertex AI Model` resource\n",
-        "- `Vertex AI Endpoint` resource\n",
+        "- `Vertex AI Model`\n",
+        "- `Vertex AI Endpoint`\n",
         "\n",
         "The steps performed include:\n",
         "\n",
@@ -112,6 +101,17 @@
         "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/latest/google_cloud_pipeline_components.aiplatform.html#module-google_cloud_pipeline_components.aiplatform)."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:beans,lcn"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the UCI Machine Learning ['Dry beans dataset'](https://archive.ics.uci.edu/ml/datasets/Dry+Bean+Dataset), from: KOKLU, M. and OZKAN, I.A., (2020), \"Multiclass Classification of Dry Beans Using Computer Vision and Machine Learning Techniques.\"In Computers and Electronics in Agriculture, 174, 105507. [DOI](https://doi.org/10.1016/j.compag.2020.105507)."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -140,7 +140,7 @@
       "source": [
         "### Set up your local development environment\n",
         "\n",
-        "If you are using Colab or Vertex AI Workbench Notebook, your environment already meets all the requirements to run this notebook. You can skip this step.\n",
+        "If you are using Colab or Vertex AI Workbench Notebook, your environment already meets all the requirements to run this notebook.\n",
         "\n",
         "Otherwise, make sure your environment meets this notebook's requirements. You need the following:\n",
         "\n",
@@ -197,9 +197,10 @@
         "if IS_WORKBENCH_NOTEBOOK:\n",
         "    USER_FLAG = \"--user\"\n",
         "\n",
-        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
-        "! pip3 install -U google-cloud-storage $USER_FLAG -q\n",
-        "! pip3 install $USER kfp google-cloud-pipeline-components --upgrade -q"
+        "! pip3 install --upgrade {USER_FLAG} google-cloud-aiplatform \\\n",
+        "                                    google-cloud-storage \\\n",
+        "                                    kfp \\\n",
+        "                                    google-cloud-pipeline-components -q"
       ]
     },
     {
@@ -237,6 +238,8 @@
         "id": "check_versions"
       },
       "source": [
+        "### Check the package versions\n",
+        "\n",
         "Check the versions of the packages you installed.  The KFP SDK version should be >=1.8."
       ]
     },
@@ -282,6 +285,17 @@
         "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$`."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "af794e75b7e3"
+      },
+      "source": [
+        "#### Set your project ID\n",
+        "\n",
+        "**If you don't know your project ID**, you may be able to get your project ID using `gcloud`."
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -290,7 +304,7 @@
       },
       "outputs": [],
       "source": [
-        "PROJECT_ID = \"python-docs-samples-tests\"  # @param {type:\"string\"}"
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
       ]
     },
     {
@@ -301,7 +315,7 @@
       },
       "outputs": [],
       "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None or PROJECT_ID == \"python-docs-samples-tests\":\n",
+        "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",
@@ -347,7 +361,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -356,9 +373,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -369,9 +386,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -382,7 +406,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated.\n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
         "when prompted to authenticate your account via oAuth.\n",
@@ -477,8 +501,8 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
-        "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -599,9 +623,6 @@
         "id": "setup_vars"
       },
       "source": [
-        "### Set up variables\n",
-        "\n",
-        "Next, set up some variables used throughout the tutorial.\n",
         "### Import libraries and define constants"
       ]
     },
@@ -613,7 +634,13 @@
       },
       "outputs": [],
       "source": [
-        "import google.cloud.aiplatform as aip"
+        "from typing import NamedTuple\n",
+        "\n",
+        "import kfp\n",
+        "from google.cloud import aiplatform\n",
+        "from kfp.v2 import dsl\n",
+        "from kfp.v2.dsl import (Artifact, ClassificationMetrics, Input, Metrics,\n",
+        "                        Output, component)"
       ]
     },
     {
@@ -624,9 +651,9 @@
       "source": [
         "#### Vertex AI constants\n",
         "\n",
-        "Setup up the following constants for Vertex AI:\n",
-        "\n",
-        "- `API_ENDPOINT`: The Vertex AI API service endpoint for `Dataset`, `Model`, `Job`, `Pipeline` and `Endpoint` services."
+        "Setup up the following constants for Vertex AI Pipeline:\n",
+        "- `PIPELINE_NAME`: Set name for the Pipeline.\n",
+        "- `PIPELINE_ROOT`: Cloud Storage bucket path to store pipeline artifacts."
       ]
     },
     {
@@ -637,65 +664,18 @@
       },
       "outputs": [],
       "source": [
-        "# API service endpoint\n",
-        "API_ENDPOINT = \"{}-aiplatform.googleapis.com\".format(REGION)"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pipeline_constants"
-      },
-      "source": [
-        "#### Vertex AI Pipelines constants\n",
-        "\n",
-        "Setup up the following constants for Vertex AI Pipelines:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pipeline_constants"
-      },
-      "outputs": [],
-      "source": [
+        "# set path for storing the pipeline artifacts\n",
+        "PIPELINE_NAME = \"automl-tabular-beans-training\"\n",
         "PIPELINE_ROOT = \"{}/pipeline_root/beans\".format(BUCKET_URI)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "additional_imports"
-      },
-      "source": [
-        "Additional imports."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "import_pipelines"
-      },
-      "outputs": [],
-      "source": [
-        "from typing import NamedTuple\n",
-        "\n",
-        "import kfp\n",
-        "from google_cloud_pipeline_components import aiplatform as gcc_aip\n",
-        "from kfp.v2 import dsl\n",
-        "from kfp.v2.dsl import (Artifact, ClassificationMetrics, Input, Metrics,\n",
-        "                        Output, component)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "init_aip:mbsdk"
       },
       "source": [
-        "## Initialize Vertex AI SDK for Python\n",
+        "### Initialize Vertex AI SDK for Python\n",
         "\n",
         "Initialize the Vertex AI SDK for Python for your project and corresponding bucket."
       ]
@@ -708,7 +688,7 @@
       },
       "outputs": [],
       "source": [
-        "aip.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
+        "aiplatform.init(project=PROJECT_ID, staging_bucket=BUCKET_URI)"
       ]
     },
     {
@@ -751,8 +731,7 @@
         ")\n",
         "def classification_model_eval_metrics(\n",
         "    project: str,\n",
-        "    location: str,  # \"us-central1\",\n",
-        "    api_endpoint: str,  # \"us-central1-aiplatform.googleapis.com\",\n",
+        "    location: str,\n",
         "    thresholds_dict_str: str,\n",
         "    model: Input[Artifact],\n",
         "    metrics: Output[Metrics],\n",
@@ -762,20 +741,21 @@
         "    import json\n",
         "    import logging\n",
         "\n",
-        "    from google.cloud import aiplatform as aip\n",
+        "    from google.cloud import aiplatform\n",
+        "\n",
+        "    aiplatform.init(project=project)\n",
         "\n",
         "    # Fetch model eval info\n",
-        "    def get_eval_info(client, model_name):\n",
-        "        from google.protobuf.json_format import MessageToDict\n",
-        "\n",
-        "        response = client.list_model_evaluations(parent=model_name)\n",
+        "    def get_eval_info(model):\n",
+        "        response = model.list_model_evaluations()\n",
         "        metrics_list = []\n",
         "        metrics_string_list = []\n",
         "        for evaluation in response:\n",
+        "            evaluation = evaluation.to_dict()\n",
         "            print(\"model_evaluation\")\n",
-        "            print(\" name:\", evaluation.name)\n",
-        "            print(\" metrics_schema_uri:\", evaluation.metrics_schema_uri)\n",
-        "            metrics = MessageToDict(evaluation._pb.metrics)\n",
+        "            print(\" name:\", evaluation[\"name\"])\n",
+        "            print(\" metrics_schema_uri:\", evaluation[\"metricsSchemaUri\"])\n",
+        "            metrics = evaluation[\"metrics\"]\n",
         "            for metric in metrics.keys():\n",
         "                logging.info(\"metric: %s, value: %s\", metric, metrics[metric])\n",
         "            metrics_str = json.dumps(metrics)\n",
@@ -783,7 +763,7 @@
         "            metrics_string_list.append(metrics_str)\n",
         "\n",
         "        return (\n",
-        "            evaluation.name,\n",
+        "            evaluation[\"name\"],\n",
         "            metrics_list,\n",
         "            metrics_string_list,\n",
         "        )\n",
@@ -832,20 +812,18 @@
         "            if metric != \"confidenceMetrics\":\n",
         "                val_string = json.dumps(metrics_list[0][metric])\n",
         "                metrics.log_metric(metric, val_string)\n",
-        "        # metrics.metadata[\"model_type\"] = \"AutoML Tabular classification\"\n",
         "\n",
         "    logging.getLogger().setLevel(logging.INFO)\n",
-        "    aip.init(project=project)\n",
+        "\n",
         "    # extract the model resource name from the input Model Artifact\n",
         "    model_resource_path = model.metadata[\"resourceName\"]\n",
         "    logging.info(\"model path: %s\", model_resource_path)\n",
         "\n",
-        "    client_options = {\"api_endpoint\": api_endpoint}\n",
-        "    # Initialize client that will be used to create and send requests.\n",
-        "    client = aip.gapic.ModelServiceClient(client_options=client_options)\n",
-        "    eval_name, metrics_list, metrics_str_list = get_eval_info(\n",
-        "        client, model_resource_path\n",
-        "    )\n",
+        "    # Get the trained model resource\n",
+        "    model = aiplatform.Model(model_resource_path)\n",
+        "\n",
+        "    # Get model evaluation metrics from the the trained model\n",
+        "    eval_name, metrics_list, metrics_str_list = get_eval_info(model)\n",
         "    logging.info(\"got evaluation name: %s\", eval_name)\n",
         "    logging.info(\"got metrics list: %s\", metrics_list)\n",
         "    log_metrics(metrics_list, metricsc)\n",
@@ -867,7 +845,9 @@
         "id": "define_pipeline:gcpc,beans,lcn"
       },
       "source": [
-        "## Define an AutoML tabular classification pipeline that uses components from `google_cloud_pipeline_components`"
+        "## Define pipeline \n",
+        "\n",
+        "Define the pipeline for AutoML tabular classification using the components from `google_cloud_pipeline_components`."
       ]
     },
     {
@@ -878,30 +858,34 @@
       },
       "outputs": [],
       "source": [
-        "DISPLAY_NAME = \"automl-beans{}\".format(TIMESTAMP)\n",
-        "PIPELINE_NAME = \"automl-tabular-beans-training-v2\"\n",
-        "MACHINE_TYPE = \"n1-standard-4\"\n",
-        "\n",
-        "\n",
         "@kfp.dsl.pipeline(name=PIPELINE_NAME, pipeline_root=PIPELINE_ROOT)\n",
         "def pipeline(\n",
-        "    bq_source: str = \"bq://aju-dev-demos.beans.beans1\",\n",
-        "    display_name: str = DISPLAY_NAME,\n",
-        "    project: str = PROJECT_ID,\n",
-        "    gcp_region: str = REGION,\n",
-        "    api_endpoint: str = API_ENDPOINT,\n",
-        "    thresholds_dict_str: str = '{\"auRoc\": 0.95}',\n",
+        "    bq_source: str,\n",
+        "    DATASET_DISPLAY_NAME: str,\n",
+        "    TRAINING_DISPLAY_NAME: str,\n",
+        "    MODEL_DISPLAY_NAME: str,\n",
+        "    ENDPOINT_DISPLAY_NAME: str,\n",
+        "    MACHINE_TYPE: str,\n",
+        "    project: str,\n",
+        "    gcp_region: str,\n",
+        "    thresholds_dict_str: str,\n",
         "):\n",
-        "    dataset_create_op = gcc_aip.TabularDatasetCreateOp(\n",
-        "        project=project, display_name=display_name, bq_source=bq_source\n",
+        "\n",
+        "    from google_cloud_pipeline_components.aiplatform import (\n",
+        "        AutoMLTabularTrainingJobRunOp, EndpointCreateOp, ModelDeployOp,\n",
+        "        TabularDatasetCreateOp)\n",
+        "\n",
+        "    dataset_create_op = TabularDatasetCreateOp(\n",
+        "        project=project, display_name=DATASET_DISPLAY_NAME, bq_source=bq_source\n",
         "    )\n",
         "\n",
-        "    training_op = gcc_aip.AutoMLTabularTrainingJobRunOp(\n",
+        "    training_op = AutoMLTabularTrainingJobRunOp(\n",
         "        project=project,\n",
-        "        display_name=display_name,\n",
+        "        display_name=TRAINING_DISPLAY_NAME,\n",
         "        optimization_prediction_type=\"classification\",\n",
         "        optimization_objective=\"minimize-log-loss\",\n",
         "        budget_milli_node_hours=1000,\n",
+        "        model_display_name=MODEL_DISPLAY_NAME,\n",
         "        column_specs={\n",
         "            \"Area\": \"numeric\",\n",
         "            \"Perimeter\": \"numeric\",\n",
@@ -924,10 +908,10 @@
         "        dataset=dataset_create_op.outputs[\"dataset\"],\n",
         "        target_column=\"Class\",\n",
         "    )\n",
+        "\n",
         "    model_eval_task = classification_model_eval_metrics(\n",
         "        project,\n",
         "        gcp_region,\n",
-        "        api_endpoint,\n",
         "        thresholds_dict_str,\n",
         "        training_op.outputs[\"model\"],\n",
         "    )\n",
@@ -937,13 +921,13 @@
         "        name=\"deploy_decision\",\n",
         "    ):\n",
         "\n",
-        "        endpoint_op = gcc_aip.EndpointCreateOp(\n",
+        "        endpoint_op = EndpointCreateOp(\n",
         "            project=project,\n",
         "            location=gcp_region,\n",
-        "            display_name=\"train-automl-beans\",\n",
+        "            display_name=ENDPOINT_DISPLAY_NAME,\n",
         "        )\n",
         "\n",
-        "        gcc_aip.ModelDeployOp(\n",
+        "        ModelDeployOp(\n",
         "            model=training_op.outputs[\"model\"],\n",
         "            endpoint=endpoint_op.outputs[\"endpoint\"],\n",
         "            dedicated_resources_min_replica_count=1,\n",
@@ -960,7 +944,7 @@
       "source": [
         "## Compile the pipeline\n",
         "\n",
-        "Next, compile the pipeline."
+        "Next, compile the pipeline to the specified json file."
       ]
     },
     {
@@ -971,11 +955,11 @@
       },
       "outputs": [],
       "source": [
-        "from kfp.v2 import compiler  # noqa: F811\n",
+        "from kfp.v2 import compiler\n",
         "\n",
         "compiler.Compiler().compile(\n",
         "    pipeline_func=pipeline,\n",
-        "    package_path=\"tabular classification_pipeline.json\".replace(\" \", \"_\"),\n",
+        "    package_path=\"tabular_classification_pipeline.json\",\n",
         ")"
       ]
     },
@@ -987,7 +971,53 @@
       "source": [
         "## Run the pipeline\n",
         "\n",
-        "Next, run the pipeline."
+        "Next, pass the input parameters required for the pipeline and run it. The defined pipeline takes the following parameters:\n",
+        "\n",
+        "- `bq_source`: BigQuery source for the tabular dataset.\n",
+        "- `DATASET_DISPLAY_NAME`: Display name for the Vertex AI managed dataset.\n",
+        "- `TRAINIG_DISPLAY_NAME`: Display name for the AutoML Training job.\n",
+        "- `MODEL_DISPLAY_NAME`: Display name for the Vertex AI Model generated as a result of the training job.\n",
+        "- `ENDPOINT_DISPLAY_NAME`: Display name for the Vertex AI Endpoint where the model is deployed.\n",
+        "- `MACHINE_TYPE`: Machine type for the serving container.\n",
+        "- `project`: Project-id where the pipeline is run.\n",
+        "- `gcp_region`: Region for setting the pipeline location.\n",
+        "- `thresholds_dict_str`: dictionary of thresholds based on which the model deployment is conditioned.\n",
+        "- `pipeline_root`:  To override the pipeline root path specified in the pipeline job's definition, specify a path that your pipeline job can access, such as a Cloud Storage bucket URI."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "1adf9b056954"
+      },
+      "outputs": [],
+      "source": [
+        "# Set the display-names for Vertex AI resources\n",
+        "PIPELINE_DISPLAY_NAME = \"[your-pipeline-display-name]\"  # @param {type:\"string\"}\n",
+        "DATASET_DISPLAY_NAME = \"[your-dataset-display-name]\"  # @param {type:\"string\"}\n",
+        "MODEL_DISPLAY_NAME = \"[your-model-display-name]\"  # @param {type:\"string\"}\n",
+        "TRAINING_DISPLAY_NAME = \"[your-training-job-display-name]\"  # @param {type:\"string\"}\n",
+        "ENDPOINT_DISPLAY_NAME = \"[your-endpoint-display-name]\"  # @param {type:\"string\"}\n",
+        "\n",
+        "# Otherwise, use the default display-names\n",
+        "if PIPELINE_DISPLAY_NAME == \"[your-pipeline-display-name]\":\n",
+        "    PIPELINE_DISPLAY_NAME = f\"pipeline_beans_{UUID}\"\n",
+        "\n",
+        "if DATASET_DISPLAY_NAME == \"[your-dataset-display-name]\":\n",
+        "    DATASET_DISPLAY_NAME = f\"dataset_beans_{UUID}\"\n",
+        "\n",
+        "if MODEL_DISPLAY_NAME == \"[your-model-display-name]\":\n",
+        "    MODEL_DISPLAY_NAME = f\"model_beans_{UUID}\"\n",
+        "\n",
+        "if TRAINING_DISPLAY_NAME == \"[your-training-job-display-name]\":\n",
+        "    TRAINING_DISPLAY_NAME = f\"automl_training_beans_{UUID}\"\n",
+        "\n",
+        "if ENDPOINT_DISPLAY_NAME == \"[your-endpoint-display-name]\":\n",
+        "    ENDPOINT_DISPLAY_NAME = f\"endpoint_beans_{UUID}\"\n",
+        "\n",
+        "# Set machine type\n",
+        "MACHINE_TYPE = \"n1-standard-4\""
       ]
     },
     {
@@ -998,19 +1028,24 @@
       },
       "outputs": [],
       "source": [
-        "DISPLAY_NAME = \"beans_\" + TIMESTAMP\n",
-        "\n",
-        "job = aip.PipelineJob(\n",
-        "    display_name=DISPLAY_NAME,\n",
-        "    template_path=\"tabular classification_pipeline.json\".replace(\" \", \"_\"),\n",
+        "# Configure the pipeline\n",
+        "job = aiplatform.PipelineJob(\n",
+        "    display_name=PIPELINE_DISPLAY_NAME,\n",
+        "    template_path=\"tabular_classification_pipeline.json\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
-        "    parameter_values={\"project\": PROJECT_ID, \"display_name\": DISPLAY_NAME},\n",
+        "    parameter_values={\n",
+        "        \"project\": PROJECT_ID,\n",
+        "        \"gcp_region\": REGION,\n",
+        "        \"bq_source\": \"bq://aju-dev-demos.beans.beans1\",\n",
+        "        \"thresholds_dict_str\": '{\"auRoc\": 0.95}',\n",
+        "        \"DATASET_DISPLAY_NAME\": DATASET_DISPLAY_NAME,\n",
+        "        \"TRAINING_DISPLAY_NAME\": TRAINING_DISPLAY_NAME,\n",
+        "        \"MODEL_DISPLAY_NAME\": MODEL_DISPLAY_NAME,\n",
+        "        \"ENDPOINT_DISPLAY_NAME\": ENDPOINT_DISPLAY_NAME,\n",
+        "        \"MACHINE_TYPE\": MACHINE_TYPE,\n",
+        "    },\n",
         "    enable_caching=False,\n",
-        ")\n",
-        "\n",
-        "job.submit()\n",
-        "\n",
-        "! rm tabular_classification_pipeline.json"
+        ")"
       ]
     },
     {
@@ -1019,11 +1054,19 @@
         "id": "view_pipeline_run:model"
       },
       "source": [
-        "Click on the generated link to see your run in the Cloud Console.\n",
-        "\n",
-        "<!-- It should look something like this as it is running:\n",
-        "\n",
-        "<a href=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/mp/automl_tabular_classif.png\" width=\"40%\"/></a> -->"
+        "Run the pipeline job. Click on the generated link to see your run in the Cloud Console."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "114ab8ff24ac"
+      },
+      "outputs": [],
+      "source": [
+        "# Run the job\n",
+        "job.run()"
       ]
     },
     {
@@ -1045,7 +1088,7 @@
       },
       "outputs": [],
       "source": [
-        "pipeline_df = aip.get_pipeline_df(pipeline=PIPELINE_NAME)\n",
+        "pipeline_df = aiplatform.get_pipeline_df(pipeline=PIPELINE_NAME)\n",
         "print(pipeline_df.head(2))"
       ]
     },
@@ -1055,21 +1098,14 @@
         "id": "cleanup:pipelines"
       },
       "source": [
-        "# Cleaning up\n",
+        "## 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 -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n",
+        "Otherwise, you can delete the individual resources you created in this tutorial:\n",
         "\n",
-        "- Dataset\n",
-        "- Pipeline\n",
-        "- Model\n",
-        "- Endpoint\n",
-        "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
-        "- Cloud Storage Bucket"
+        "(Set `delete_bucket` to **True** to delete the Cloud Storage bucket.)"
       ]
     },
     {
@@ -1080,94 +1116,37 @@
       },
       "outputs": [],
       "source": [
-        "delete_dataset = True\n",
-        "delete_pipeline = True\n",
-        "delete_model = True\n",
-        "delete_endpoint = True\n",
-        "delete_batchjob = True\n",
-        "delete_customjob = True\n",
-        "delete_hptjob = True\n",
-        "delete_bucket = True\n",
+        "delete_bucket = False\n",
         "\n",
-        "try:\n",
-        "    if delete_model and \"DISPLAY_NAME\" in globals():\n",
-        "        models = aip.Model.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        model = models[0]\n",
-        "        aip.Model.delete(model)\n",
-        "        print(\"Deleted model:\", model)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the Vertex AI Pipeline Job\n",
+        "job.delete()\n",
         "\n",
-        "try:\n",
-        "    if delete_endpoint and \"DISPLAY_NAME\" in globals():\n",
-        "        endpoints = aip.Endpoint.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        endpoint = endpoints[0]\n",
-        "        endpoint.undeploy_all()\n",
-        "        aip.Endpoint.delete(endpoint.resource_name)\n",
-        "        print(\"Deleted endpoint:\", endpoint)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the Vertex AI Endpoint\n",
+        "endpoints = aiplatform.Endpoint.list(\n",
+        "    filter=f\"display_name={ENDPOINT_DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        ")\n",
         "\n",
-        "if delete_dataset and \"DISPLAY_NAME\" in globals():\n",
-        "    if \"tabular\" == \"tabular\":\n",
-        "        try:\n",
-        "            datasets = aip.TabularDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.TabularDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
+        "if len(endpoints) > 0:\n",
+        "    endpoint = endpoints[0]\n",
+        "    endpoint.delete(force=True)\n",
         "\n",
-        "    if \"tabular\" == \"image\":\n",
-        "        try:\n",
-        "            datasets = aip.ImageDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.ImageDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
+        "# Delete the Vertex AI model\n",
+        "models = aiplatform.Model.list(\n",
+        "    filter=f\"display_name={MODEL_DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        ")\n",
+        "if len(models) > 0:\n",
+        "    model = models[0]\n",
+        "    model.delete()\n",
         "\n",
-        "    if \"tabular\" == \"text\":\n",
-        "        try:\n",
-        "            datasets = aip.TextDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.TextDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "    if \"tabular\" == \"video\":\n",
-        "        try:\n",
-        "            datasets = aip.VideoDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.VideoDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "try:\n",
-        "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",
-        "        pipelines = aip.PipelineJob.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        pipeline = pipelines[0]\n",
-        "        aip.PipelineJob.delete(pipeline.resource_name)\n",
-        "        print(\"Deleted pipeline:\", pipeline)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "# Delete the Vertex AI Dataset\n",
+        "datasets = aiplatform.TabularDataset.list(\n",
+        "    filter=f\"display_name={DATASET_DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        ")\n",
+        "if len(datasets) > 0:\n",
+        "    dataset = datasets[0]\n",
+        "    dataset.delete()\n",
         "\n",
+        "# Delete the Cloud Storage bucket\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]

notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb

@@ -64,19 +64,6 @@
         "This notebook shows how to use the components defined in [`google_cloud_pipeline_components`](https://github.com/kubeflow/pipelines/tree/master/components/google-cloud) to build an AutoML tabular regression workflow on [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines)."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:cal_housing,lrg"
-      },
-      "source": [
-        "### Dataset\n",
-        "\n",
-        "The dataset used for this tutorial is the [California Housing dataset from the 1990 Census](https://developers.google.com/machine-learning/crash-course/california-housing-data-description)\n",
-        "\n",
-        "The dataset predicts the median house price."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -109,6 +96,19 @@
         "The components are [documented here](https://google-cloud-pipeline-components.readthedocs.io/en/latest/google_cloud_pipeline_components.aiplatform.html#module-google_cloud_pipeline_components.aiplatform)."
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "77de5c53ac91"
+      },
+      "source": [
+        "### Dataset\n",
+        "\n",
+        "The dataset used for this tutorial is the [California Housing dataset from the 1990 Census](https://developers.google.com/machine-learning/crash-course/california-housing-data-description)\n",
+        "\n",
+        "The dataset predicts the median house price."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -234,6 +234,8 @@
         "id": "check_versions"
       },
       "source": [
+        "### Check installed package versions\n",
+        "\n",
         "Check the versions of the packages you installed.  The KFP SDK version should be >=1.6."
       ]
     },
@@ -344,7 +346,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -353,9 +358,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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 the uuid onto the name of resources you create in this tutorial."
       ]
     },
     {
@@ -366,9 +371,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\n",
+        "# Generate a uuid of length 8\n",
+        "def generate_uuid():\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=8))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -474,7 +486,7 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
         "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
       ]
     },
@@ -718,10 +730,10 @@
         "    endpoint_op = EndpointCreateOp(\n",
         "        project=project,\n",
         "        location=region,\n",
-        "        display_name=\"train-automl-flowers\",\n",
+        "        display_name=\"train-automl-cal_housing_endpoint\",\n",
         "    )\n",
         "\n",
-        "    ModelDeployOp(\n",
+        "    _ = ModelDeployOp(\n",
         "        model=training_op.outputs[\"model\"],\n",
         "        endpoint=endpoint_op.outputs[\"endpoint\"],\n",
         "        dedicated_resources_machine_type=\"n1-standard-4\",\n",
@@ -753,7 +765,7 @@
         "\n",
         "compiler.Compiler().compile(\n",
         "    pipeline_func=pipeline,\n",
-        "    package_path=\"tabular regression_pipeline.json\".replace(\" \", \"_\"),\n",
+        "    package_path=\"tabular_regression_pipeline.json\",\n",
         ")"
       ]
     },
@@ -776,11 +788,11 @@
       },
       "outputs": [],
       "source": [
-        "DISPLAY_NAME = \"cal_housing_\" + TIMESTAMP\n",
+        "DISPLAY_NAME = \"cal_housing_\" + UUID\n",
         "\n",
         "job = aip.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
-        "    template_path=\"tabular regression_pipeline.json\".replace(\" \", \"_\"),\n",
+        "    template_path=\"tabular_regression_pipeline.json\",\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         "    enable_caching=False,\n",
         ")\n",
@@ -818,16 +830,7 @@
         "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 -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n",
-        "\n",
-        "- Dataset\n",
-        "- Pipeline\n",
-        "- Model\n",
-        "- Endpoint\n",
-        "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
-        "- Cloud Storage Bucket"
+        "Otherwise, you can delete the individual resources you created in this tutorial "
       ]
     },
     {
@@ -842,89 +845,73 @@
         "delete_pipeline = True\n",
         "delete_model = True\n",
         "delete_endpoint = True\n",
-        "delete_batchjob = True\n",
-        "delete_customjob = True\n",
-        "delete_hptjob = True\n",
         "delete_bucket = True\n",
         "\n",
-        "try:\n",
-        "    if delete_model and \"DISPLAY_NAME\" in globals():\n",
-        "        models = aip.Model.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        model = models[0]\n",
-        "        aip.Model.delete(model)\n",
-        "        print(\"Deleted model:\", model)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "dataset_display_name = \"housing\"\n",
+        "pipeline_display_name = \"automl-tab-training-v2\"\n",
+        "model_display_name = \"train-automl-cal_housing\"\n",
+        "endpoint_display_name = \"train-automl-cal_housing_endpoint\"\n",
         "\n",
-        "try:\n",
-        "    if delete_endpoint and \"DISPLAY_NAME\" in globals():\n",
-        "        endpoints = aip.Endpoint.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n",
-        "        )\n",
+        "\n",
+        "if delete_endpoint:\n",
+        "    endpoints = aip.Endpoint.list(\n",
+        "        filter=f\"display_name={endpoint_display_name}\", order_by=\"create_time\"\n",
+        "    )\n",
+        "    if endpoints:\n",
         "        endpoint = endpoints[0]\n",
         "        endpoint.undeploy_all()\n",
-        "        aip.Endpoint.delete(endpoint.resource_name)\n",
+        "        endpoint.delete()\n",
         "        print(\"Deleted endpoint:\", endpoint)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
         "\n",
-        "if delete_dataset and \"DISPLAY_NAME\" in globals():\n",
+        "if delete_model:\n",
+        "    models = aip.Model.list(\n",
+        "        filter=f\"display_name={model_display_name}\", order_by=\"create_time\"\n",
+        "    )\n",
+        "    if models:\n",
+        "        model = models[0]\n",
+        "        model.delete()\n",
+        "        print(\"Deleted model:\", model)\n",
+        "\n",
+        "if delete_dataset:\n",
         "    if \"tabular\" == \"tabular\":\n",
-        "        try:\n",
-        "            datasets = aip.TabularDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
+        "        datasets = aip.TabularDataset.list(\n",
+        "            filter=f\"display_name={dataset_display_name}\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        if datasets:\n",
         "            dataset = datasets[0]\n",
-        "            aip.TabularDataset.delete(dataset.resource_name)\n",
+        "            dataset.delete()\n",
         "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
         "\n",
         "    if \"tabular\" == \"image\":\n",
-        "        try:\n",
-        "            datasets = aip.ImageDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
+        "        datasets = aip.ImageDataset.list(\n",
+        "            filter=f\"display_name={dataset_display_name}\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        if datasets:\n",
         "            dataset = datasets[0]\n",
-        "            aip.ImageDataset.delete(dataset.resource_name)\n",
+        "            dataset.delete()\n",
         "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
         "\n",
         "    if \"tabular\" == \"text\":\n",
-        "        try:\n",
-        "            datasets = aip.TextDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
+        "        datasets = aip.TextDataset.list(\n",
+        "            filter=f\"display_name={dataset_display_name}\", order_by=\"create_time\"\n",
+        "        )\n",
+        "        if datasets:\n",
         "            dataset = datasets[0]\n",
-        "            aip.TextDataset.delete(dataset.resource_name)\n",
+        "            dataset.delete()\n",
         "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
         "\n",
         "    if \"tabular\" == \"video\":\n",
-        "        try:\n",
-        "            datasets = aip.VideoDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.VideoDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "try:\n",
-        "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",
-        "        pipelines = aip.PipelineJob.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
+        "        datasets = aip.VideoDataset.list(\n",
+        "            filter=f\"display_name={dataset_display_name}\", order_by=\"create_time\"\n",
         "        )\n",
-        "        pipeline = pipelines[0]\n",
-        "        aip.PipelineJob.delete(pipeline.resource_name)\n",
-        "        print(\"Deleted pipeline:\", pipeline)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "        if datasets:\n",
+        "            dataset = datasets[0]\n",
+        "            dataset.delete()\n",
+        "            print(\"Deleted dataset:\", dataset)\n",
+        "\n",
+        "if delete_pipeline:\n",
+        "    job.delete()\n",
+        "\n",
         "\n",
         "if delete_bucket and os.getenv(\"IS_TESTING\"):\n",
         "    ! gsutil rm -r $BUCKET_URI"

notebooks/official/pipelines/google_cloud_pipeline_components_dataproc_tabular.ipynb

@@ -29,7 +29,7 @@
         "id": "JAPoU8Sm5E6e"
       },
       "source": [
-        "# Vertex AI Pipelines: Loan eligibility prediction using google-cloud-pipeline-components and Spark ML\n",
+        "# Vertex AI Pipelines: Loan eligibility prediction using `google-cloud-pipeline-components` and Spark ML\n",
         "\n",
         "<table align=\"left\">\n",
         "\n",
@@ -206,7 +206,7 @@
         "    \n",
         "!pip3 install {USER_FLAG} --upgrade google-cloud-aiplatform==1.11.0 \\\n",
         "                                    kfp==1.8.11 \\\n",
-        "                                    google-cloud-pipeline-components==1.0.1 --quiet --no-warn-conflicts"
+        "                                    google-cloud-pipeline-components==1.0.18 --quiet --no-warn-conflicts"
       ]
     },
     {
@@ -733,9 +733,7 @@
         "from pathlib import Path as path\n",
         "from typing import NamedTuple\n",
         "\n",
-        "# Part 1 - ML Training\n",
         "from google.cloud import aiplatform as vertex_ai\n",
-        "from google_cloud_pipeline_components import aiplatform as vertex_ai_components\n",
         "from kfp.v2 import compiler, dsl\n",
         "from kfp.v2.dsl import (Artifact, ClassificationMetrics, Condition, Input,\n",
         "                        Metrics, Output, component)"
@@ -763,14 +761,14 @@
         "PIPELINE_ROOT = f\"{BUCKET_URI}/pipelines\"\n",
         "PIPELINE_PACKAGE_PATH = str(BUILD_PATH / f\"pipeline_{UUID}.json\")\n",
         "RUNTIME_CONTAINER_IMAGE = f\"gcr.io/{PROJECT_ID}/{RUNTIME_IMAGE}:{IMAGE_TAG}\"\n",
-        "ML_APPLICATION = \"spark\"\n",
-        "TASK = \"classifier\"\n",
+        "ML_APPLICATION = \"loan-eligibility\"\n",
+        "TASK = \"sparkml\"\n",
         "MODEL_TYPE = \"rfor\"\n",
         "VERSION = \"1.0.0\"\n",
         "MODEL_NAME = f\"{ML_APPLICATION}-{TASK}-{MODEL_TYPE}-{VERSION}\"\n",
+        "ARTIFACT_URI = f\"{BUCKET_URI}/deliverables/bundle/{UUID}\"\n",
         "\n",
         "# Preprocessing\n",
-        "PREPROCESSING_BATCH_ID = f\"data-preprocessing-{UUID}\"\n",
         "PREPROCESSING_PYTHON_FILE_URI = f\"{BUCKET_URI}/src/data_preprocessing.py\"\n",
         "PROCESSED_DATA_URI = f\"{BUCKET_URI}/data/processed\"\n",
         "PREPROCESSING_ARGS = [\n",
@@ -785,7 +783,6 @@
         "GCS_PREPROCESSED_URI = f\"{PROCESSED_DATA_URI}/*/?.csv\"\n",
         "\n",
         "# Training\n",
-        "TRAINING_BATCH_ID = f\"model-training-{UUID}\"\n",
         "TRAINING_PYTHON_FILE_URI = f\"{BUCKET_URI}/src/model_training.py\"\n",
         "MODEL_URI = f\"{BUCKET_URI}/deliverables/model/rfor/{UUID}/train_model\"\n",
         "METRICS_URI = f\"{BUCKET_URI}/deliverables/metrics/rfor/{UUID}/train_metrics.json\"\n",
@@ -800,10 +797,9 @@
         "\n",
         "# Condition\n",
         "AUPR_THRESHOLD = 0.5\n",
-        "AUPR_HYPERTUNE_CONDITION = \"[AUPR_HYPERTUNE]\"\n",
+        "AUPR_HYPERTUNE_CONDITION = \"hypertune\"\n",
         "\n",
         "# Hypertuning\n",
-        "HPT_TRAINING_BATCH_ID = f\"hyper-tuning-{UUID}\"\n",
         "HPT_PYTHON_FILE_URI = f\"{BUCKET_URI}/src/hp_tuning.py\"\n",
         "HPT_MODEL_URI = f\"{BUCKET_URI}/deliverables/model/rfor/{UUID}/model\"\n",
         "HPT_METRICS_URI = f\"{BUCKET_URI}/deliverables/metrics/rfor/{UUID}/metrics.json\"\n",
@@ -814,7 +810,24 @@
         "    HPT_MODEL_URI,\n",
         "    \"--metrics-path\",\n",
         "    HPT_METRICS_URI,\n",
-        "]"
+        "]\n",
+        "HPT_BUNDLE_URI = f\"{ARTIFACT_URI}/model.zip\"\n",
+        "HPT_ARGS = [\n",
+        "    \"--train-path\",\n",
+        "    PROCESSED_DATA_URI,\n",
+        "    \"--model-path\",\n",
+        "    HPT_MODEL_URI,\n",
+        "    \"--metrics-path\",\n",
+        "    HPT_METRICS_URI,\n",
+        "    \"--bundle-path\",\n",
+        "    HPT_BUNDLE_URI,\n",
+        "]\n",
+        "HPT_RUNTIME_PROPERTIES = {\n",
+        "    \"spark.jars.packages\": \"ml.combust.mleap:mleap-spark-base_2.12:0.20.0,ml.combust.mleap:mleap-spark_2.12:0.20.0\"\n",
+        "}\n",
+        "\n",
+        "# Deploy\n",
+        "SERVING_IMAGE_URI = f\"{REGION}-docker.pkg.dev/{PROJECT_ID}/{REPO_NAME}/spark-ml-serving\""
       ]
     },
     {
@@ -843,7 +856,7 @@
         "id": "LB2aM7VyRyZG"
       },
       "source": [
-        "## PART I - Build the Vertex Pipeline to train and deploy a Spark model\n",
+        "## Build the Vertex Pipeline to train and deploy a Spark model\n",
         "\n",
         "In this case, the ML pipeline includes the following steps:\n",
         "\n",
@@ -851,10 +864,16 @@
         "2.   Train an `RandomForestClassifier` with `DataprocPySparkBatchOp`\n",
         "3.   Run a custom component in order to evaluate the model\n",
         "\n",
-        "If the model respects the performance condition, then\n",
+        "If the model respects the performance condition, then:\n",
         "\n",
         "4.   Hypertune the `RandomForestClassifier` with `DataprocPySparkBatchOp`\n",
-        "5.   Register the model in the Vertex AI Model Registry\n"
+        "5.   Serializes the model to MLeap format to use the model outside of Spark.\n",
+        "\n",
+        "If the `deploy_model` pipeline parameter is set to `True`:\n",
+        "\n",
+        "6.   Upload the model to Vertex AI Model Registry.\n",
+        "7.   Creates a Vertex AI endpoint.\n",
+        "8.   Deploys the model to the Vertex AI endpoint for serving online prediction requests.\n"
       ]
     },
     {
@@ -1430,7 +1449,9 @@
         "\n",
         "- `--train-path`: The GCS path of the training sample.\n",
         "- `--model-path`: The GCS path to store the trained model.\n",
-        "- `--metrics-path`: The GCS path to store the metrics of model."
+        "- `--metrics-path`: The GCS path to store the metrics of model.\n",
+        "\n",
+        "The hyperparameter tuning job will also serialize the best performing model to an MLeap bundle, which can be imported to Vertex AI as a model for serving predictions - see the *Serve your model in Vertex AI* section further below."
       ]
     },
     {
@@ -1467,6 +1488,9 @@
         "except ImportError as e:\n",
         "    print('WARN: Something wrong with pyspark library. Please check configuration settings!')\n",
         "    print(e)\n",
+        "    \n",
+        "import mleap.pyspark\n",
+        "from mleap.pyspark.spark_support import SimpleSparkSerializer\n",
         "\n",
         "from pyspark.sql.types import StructType, DoubleType, StringType\n",
         "from pyspark.sql.functions import col, udf\n",
@@ -1572,6 +1596,16 @@
         "        ''',\n",
         "        type=str,\n",
         "        required=True)\n",
+        "    args_parser.add_argument(\n",
+        "        '--bundle-path',\n",
+        "        help='''\n",
+        "        The GCS path to store the exported MLeap bundle. \n",
+        "        Format: \n",
+        "        - locally: /path/to/dir\n",
+        "        - cloud: gs://bucket/path\n",
+        "        ''',\n",
+        "        type=str,\n",
+        "        required=True)\n",
         "    return args_parser.parse_args()\n",
         "\n",
         "\n",
@@ -1728,6 +1762,7 @@
         "    train_path = args.train_path\n",
         "    model_path = args.model_path\n",
         "    metrics_path = args.metrics_path\n",
+        "    bundle_path = args.bundle_path\n",
         "\n",
         "    try:\n",
         "        logger.info('initializing pipeline training.')\n",
@@ -1759,10 +1794,20 @@
         "        logger.info(f'load model pipeline in {model_path}.')\n",
         "        pipeline_model.write().overwrite().save(model_path)\n",
         "\n",
-        "        logger.info(f'Upload metrics under {metrics_path}.')\n",
+        "        logger.info(f'upload metrics under {metrics_path}.')\n",
         "        bucket = urlparse(model_path).netloc\n",
         "        metrics_file_path = urlparse(metrics_path).path.strip('/')\n",
         "        write_metrics(bucket, metrics, metrics_file_path)\n",
+        "        \n",
+        "        logger.info('export MLeap bundle to temporary location')\n",
+        "        pipeline_model.bestModel.serializeToBundle(f'jar:file:/tmp/bundle.zip', predictions)\n",
+        "        \n",
+        "        logger.info(f'upload MLeap bundle to {bundle_path}')\n",
+        "        bundle_file_path = urlparse(bundle_path).path.strip('/')\n",
+        "        bucket = urlparse(bundle_path).netloc\n",
+        "        logger.info(f'Copying /tmp/bundle.zip to bucket {bucket} using object name {bundle_file_path} ...')\n",
+        "        upload_file(bucket, '/tmp/bundle.zip', bundle_file_path)\n",
+        "        \n",
         "    except RuntimeError as main_error:\n",
         "        logger.error(main_error)\n",
         "    else:\n",
@@ -1807,11 +1852,11 @@
         "id": "68nYBB5GS9TB"
       },
       "source": [
-        "### Build a custom dataproc serverless image\n",
+        "### Build a custom Dataproc Serverless container image\n",
         "\n",
-        "The `DataprocPySparkBatchOp` allows you to pass custom image that you use when the [provided Dataproc Serverless runtime versions](https://cloud.google.com/dataproc-serverless/docs/concepts/versions/spark-runtime-versions) does not respect your requirements. \n",
+        "Dataproc Serverless provides [default runtime images](https://cloud.google.com/dataproc-serverless/docs/concepts/versions/spark-runtime-versions). You can also use custom container images for your Dataproc Serverless workloads. \n",
         "\n",
-        "**Note:** This step is optional and is included here for general awareness."
+        "The steps in this section builds a custom container image that includes additional dependencies. The custom container image can be specified when using the `DataprocPySparkBatchOp` component to launch the workload within a pipeline."
       ]
     },
     {
@@ -1820,7 +1865,7 @@
         "id": "GF9_5IGYqLAX"
       },
       "source": [
-        "#### Define the Dataproc serverless custom runtime image"
+        "#### Define the Dataproc Serverless custom runtime image"
       ]
     },
     {
@@ -1891,7 +1936,8 @@
         "      python \\\n",
         "      scikit-image \\\n",
         "      scikit-learn \\\n",
-        "      scipy  \n",
+        "      scipy \\\n",
+        "      mleap\n",
         "\n",
         "# (Required) Create the 'spark' group/user.\n",
         "# The GID and UID must be 1099. Home directory is required.\n",
@@ -1927,7 +1973,9 @@
         "id": "ZXzI2xInqb3V"
       },
       "source": [
-        "#### Build the Dataproc serverless custom runtime using Google Cloud Build"
+        "#### Build the Dataproc Serverless custom runtime using Google Cloud Build\n",
+        "\n",
+        "**Note:** this step may take approximately 5 to 10 minutes to complete."
       ]
     },
     {
@@ -2104,12 +2152,12 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "1-Ccx4uLDz4N"
+        "id": "28f3d22dd97f"
       },
       "source": [
-        "#### Model registration custom component\n",
+        "#### Create component for passing args to hyperparameter tuning component\n",
         "\n",
-        "Define a component to create a model resource for the trained model on Vertex AI Model registry."
+        "The following component passes the args `--train-path`, `--model-path` and `--metrics-path`, and `--bundle-path` in the required format for the hyperparamter tuning function defined earlier."
       ]
     },
     {
@@ -2120,22 +2168,230 @@
       },
       "outputs": [],
       "source": [
-        "# TODO: Build a custom compiler using Spark docker image to compile the Mleap bundle\n",
-        "\n",
-        "\n",
         "@component(base_image=\"python:3.8-slim\")\n",
-        "def register_model(\n",
-        "    artifact_uri: str,\n",
-        "    model: Output[Artifact],\n",
-        ") -> NamedTuple(\"Outputs\", [(\"uri\", str)]):\n",
+        "def build_hpt_args(\n",
+        "    dataset_uri: Input[Artifact],\n",
+        "    train_path: str,\n",
+        "    model_path: str,\n",
+        "    metrics_path: str,\n",
+        "    bundle_path: str,\n",
+        ") -> list:\n",
+        "    return [\n",
+        "        \"--train-path\",\n",
+        "        train_path,\n",
+        "        \"--model-path\",\n",
+        "        model_path,\n",
+        "        \"--metrics-path\",\n",
+        "        metrics_path,\n",
+        "        \"--bundle-path\",\n",
+        "        bundle_path,\n",
+        "    ]"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0db73fff95b3"
+      },
+      "source": [
+        "### (Optional) Serve your model using Vertex AI\n",
         "\n",
-        "    component_outputs = NamedTuple(\n",
-        "        \"Outputs\",\n",
-        "        [\n",
-        "            (\"uri\", str),\n",
-        "        ],\n",
-        "    )\n",
-        "    return component_outputs(artifact_uri)"
+        "The hyperparameter tuning task exports the best performing model as an MLeap bundle. The MLeap bundle can be imported into the Vertex AI Model Registry and used for prediction serving. See [Serving Spark ML model using Vertex AI](https://cloud.google.com/architecture/spark-ml-model-with-vertexai) for more information.\n",
+        "\n",
+        "Enable import of the MLeap bundle into the Vertex AI Model Registry and online prediction serving."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2d7e7c8fc21b"
+      },
+      "outputs": [],
+      "source": [
+        "# Set DEPLOY_MODEL to True\n",
+        "DEPLOY_MODEL = False"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0bb792622d9f"
+      },
+      "source": [
+        "### Build the model serving container image\n",
+        "\n",
+        "A *serving container image* is required to import your model into the Model Registry. The serving container image provides the model serving implementation for the model. The following replicates the instructions from [Serving Spark ML model using Vertex AI](https://cloud.google.com/architecture/spark-ml-model-with-vertexai) to build the serving container image.\n",
+        "\n",
+        "**Note:** this step may take approximately 5 to 10 minutes to complete."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "4703a0f969a3"
+      },
+      "outputs": [],
+      "source": [
+        "DEPLOY_MODEL_CONDITION = 'deploy'\n",
+        "\n",
+        "if DEPLOY_MODEL:\n",
+        "\n",
+        "    import os\n",
+        "    \n",
+        "    CWD = os.getcwd()\n",
+        "\n",
+        "    # Clone and build the scala-sbt cloud builder\n",
+        "    ! git clone https://github.com/GoogleCloudPlatform/cloud-builders-community.git\n",
+        "    ! cd ${CWD}/cloud-builders-community/scala-sbt && \\\n",
+        "        gcloud builds submit .\n",
+        "\n",
+        "    # Clone and build the serving container code\n",
+        "    ! cd {CWD} && git clone https://github.com/GoogleCloudPlatform/vertex-ai-spark-ml-serving.git\n",
+        "    ! cd {CWD}/vertex-ai-spark-ml-serving && \\\n",
+        "        gcloud builds submit --config=cloudbuild.yaml \\\n",
+        "            --substitutions=\"_LOCATION={REGION},_REPOSITORY={REPO_NAME},_IMAGE=spark-ml-serving\" ."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "947b51adc087"
+      },
+      "source": [
+        "### Create component for importing a model artifact into a pipeline\n",
+        "\n",
+        "The pipeline uses the `ModelImportOp` component to import (upload) a model to Vertex AI Model Registry.\n",
+        "\n",
+        "The `import_model_artifact` python component creates a model artifact that can be passed to the `ModelImportOp` component."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "2ed96e7ad046"
+      },
+      "outputs": [],
+      "source": [
+        "@dsl.component(\n",
+        "    base_image=\"python:3.8-slim\",\n",
+        "    packages_to_install=[\"google-cloud-aiplatform\"],\n",
+        ")\n",
+        "def import_model_artifact(\n",
+        "    model: dsl.Output[dsl.Artifact], artifact_uri: str, serving_image_uri: str\n",
+        "):\n",
+        "    model.metadata[\"containerSpec\"] = {\n",
+        "        \"imageUri\": serving_image_uri,\n",
+        "        \"healthRoute\": \"/health\",\n",
+        "        \"predictRoute\": \"/predict\",\n",
+        "    }\n",
+        "    model.uri = artifact_uri"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0d83d9e80923"
+      },
+      "source": [
+        "The serving container requires the model schema in JSON format, which is read during container startup. See [Provide the model schema](https://cloud.google.com/architecture/spark-ml-model-with-vertexai#provide_the_model_schema) for more information.\n",
+        "\n",
+        "Write the model schema file:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "521d2f4d7992"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile $SRC/schema.json\n",
+        "{\n",
+        "  \"input\": [\n",
+        "    {\n",
+        "      \"name\": \"loan_amount\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"loan_term\",\n",
+        "      \"type\": \"STRING\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"property_area\",\n",
+        "      \"type\": \"STRING\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_7\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_3\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_1\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_9\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_5\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_0\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_8\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_4\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_2\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    },\n",
+        "    {\n",
+        "      \"name\": \"feature_6\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    }\n",
+        "  ],\n",
+        "  \"output\": [\n",
+        "    {\n",
+        "      \"name\": \"prediction\",\n",
+        "      \"type\": \"DOUBLE\"\n",
+        "    }\n",
+        "  ]\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d0b88e26570a"
+      },
+      "source": [
+        "Copy the model schema configuration file to GCS. The serving container reads the model schema file location from the `AIP_STORAGE_URI` environment at startup. See [Import the model into Vertex AI](https://cloud.google.com/architecture/spark-ml-model-with-vertexai#import-the-model-into-vertex-ai) for more information."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b7763bb558f3"
+      },
+      "outputs": [],
+      "source": [
+        "! gsutil cp $SRC/schema.json $ARTIFACT_URI/schema.json"
       ]
     },
     {
@@ -2159,30 +2415,35 @@
       "source": [
         "@dsl.pipeline(name=PIPELINE_NAME, description=\"A pipeline to train a PySpark model.\")\n",
         "def pipeline(\n",
-        "    preprocessing_batch_id: str = PREPROCESSING_BATCH_ID,\n",
         "    preprocessing_main_python_file_uri: str = PREPROCESSING_PYTHON_FILE_URI,\n",
         "    train_data_path: str = FEATURES_TRAIN_URI,\n",
         "    preprocessed_data_path: str = PROCESSED_DATA_URI,\n",
         "    dataset_name: str = DATASET_NAME,\n",
         "    dataset_uri: str = GCS_PREPROCESSED_URI,\n",
-        "    training_batch_id: str = TRAINING_BATCH_ID,\n",
         "    training_main_python_file_uri: str = TRAINING_PYTHON_FILE_URI,\n",
         "    train_path: str = PROCESSED_DATA_URI,\n",
         "    model_path: str = MODEL_URI,\n",
         "    metrics_path: str = METRICS_URI,\n",
         "    threshold: float = AUPR_THRESHOLD,\n",
-        "    hpt_batch_id: str = HPT_TRAINING_BATCH_ID,\n",
         "    hpt_main_python_file_uri: str = HPT_PYTHON_FILE_URI,\n",
         "    hpt_model_path: str = HPT_MODEL_URI,\n",
         "    hpt_metrics_path: str = HPT_METRICS_URI,\n",
+        "    hpt_bundle_path: str = HPT_BUNDLE_URI,\n",
         "    custom_container_image: str = RUNTIME_CONTAINER_IMAGE,\n",
         "    model_name: str = MODEL_NAME,\n",
         "    project_id: str = PROJECT_ID,\n",
         "    location: str = REGION,\n",
+        "    deploy_model: bool = DEPLOY_MODEL,\n",
+        "    artifact_uri: str = ARTIFACT_URI,\n",
+        "    serving_image_uri: str = SERVING_IMAGE_URI,\n",
         "):\n",
-        "\n",
-        "    from google_cloud_pipeline_components.experimental.dataproc import \\\n",
+        "    from google_cloud_pipeline_components.v1.dataproc import \\\n",
         "        DataprocPySparkBatchOp\n",
+        "    from google_cloud_pipeline_components.v1.dataset import \\\n",
+        "        TabularDatasetCreateOp\n",
+        "    from google_cloud_pipeline_components.v1.endpoint import (EndpointCreateOp,\n",
+        "                                                              ModelDeployOp)\n",
+        "    from google_cloud_pipeline_components.v1.model import ModelUploadOp\n",
         "\n",
         "    # build preprocessed data args\n",
         "    build_preprocessing_args_op = build_preprocessing_args(\n",
@@ -2194,13 +2455,12 @@
         "        project=project_id,\n",
         "        location=location,\n",
         "        container_image=custom_container_image,\n",
-        "        batch_id=preprocessing_batch_id,\n",
         "        main_python_file_uri=preprocessing_main_python_file_uri,\n",
         "        args=build_preprocessing_args_op.output,\n",
         "    ).after(build_preprocessing_args_op)\n",
         "\n",
         "    # create dataset\n",
-        "    create_dataset_op = vertex_ai_components.TabularDatasetCreateOp(\n",
+        "    create_dataset_op = TabularDatasetCreateOp(\n",
         "        display_name=dataset_name,\n",
         "        gcs_source=dataset_uri,\n",
         "        project=project_id,\n",
@@ -2220,7 +2480,6 @@
         "        project=project_id,\n",
         "        location=location,\n",
         "        container_image=custom_container_image,\n",
-        "        batch_id=training_batch_id,\n",
         "        main_python_file_uri=training_main_python_file_uri,\n",
         "        args=build_training_args_op.output,\n",
         "    ).after(build_training_args_op)\n",
@@ -2233,11 +2492,12 @@
         "        name=AUPR_HYPERTUNE_CONDITION,\n",
         "    ):\n",
         "\n",
-        "        build_hpt_args_op = build_training_args(\n",
+        "        build_hpt_args_op = build_hpt_args(\n",
         "            dataset_uri=create_dataset_op.output,\n",
         "            train_path=train_path,\n",
         "            model_path=hpt_model_path,\n",
         "            metrics_path=hpt_metrics_path,\n",
+        "            bundle_path=hpt_bundle_path,\n",
         "        ).after(evaluate_model_op)\n",
         "\n",
         "        # hyperparameter tuning\n",
@@ -2245,13 +2505,46 @@
         "            project=project_id,\n",
         "            location=location,\n",
         "            container_image=custom_container_image,\n",
-        "            batch_id=hpt_batch_id,\n",
         "            main_python_file_uri=hpt_main_python_file_uri,\n",
         "            args=build_hpt_args_op.output,\n",
+        "            runtime_config_properties=HPT_RUNTIME_PROPERTIES,\n",
         "        ).after(model_traning_op)\n",
         "\n",
-        "        # upload model\n",
-        "        register_model(artifact_uri=hpt_model_path).after(hyperparameter_tuning_op)"
+        "        # evaluate condition to upload and deploy model to Vertex AI\n",
+        "        with Condition(\n",
+        "            # kfp casts `bool` parameter to `str`\n",
+        "            deploy_model == \"True\",\n",
+        "            name=DEPLOY_MODEL_CONDITION,\n",
+        "        ):\n",
+        "            # import the model into the pipeline as a kfp model artifact\n",
+        "            import_model_artifact_op = import_model_artifact(\n",
+        "                artifact_uri=artifact_uri,\n",
+        "                serving_image_uri=serving_image_uri,\n",
+        "            )\n",
+        "\n",
+        "            # upload model to Vertex AI\n",
+        "            model_upload_op = ModelUploadOp(\n",
+        "                project=project_id,\n",
+        "                location=location,\n",
+        "                display_name=model_name,\n",
+        "                unmanaged_container_model=import_model_artifact_op.outputs[\"model\"],\n",
+        "            ).after(hyperparameter_tuning_op)\n",
+        "\n",
+        "            # create a serving endpoint\n",
+        "            endpoint_op = EndpointCreateOp(\n",
+        "                project=project_id,\n",
+        "                location=location,\n",
+        "                display_name=model_name,\n",
+        "            ).after(model_upload_op)\n",
+        "\n",
+        "            # deploy  model to the serving endpoint\n",
+        "            _ = ModelDeployOp(\n",
+        "                model=model_upload_op.outputs[\"model\"],\n",
+        "                endpoint=endpoint_op.outputs[\"endpoint\"],\n",
+        "                dedicated_resources_machine_type=\"n1-standard-2\",\n",
+        "                dedicated_resources_min_replica_count=1,\n",
+        "                dedicated_resources_max_replica_count=1,\n",
+        "            ).after(endpoint_op)"
       ]
     },
     {
@@ -2327,6 +2620,65 @@
         "pipeline.wait()"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b584afa5a1b1"
+      },
+      "source": [
+        "### (Optional) Get online predictions from the deployed model\n",
+        "\n",
+        "You can request online predictions if the model was deployed to a Vertex AI endpoint. Use the `google-cloud-aiplatform` client library to request predictions, or use `curl` as per below:\n",
+        "\n",
+        "Create the prediction request payload with the instances that you want to predict:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "12d068e1877c"
+      },
+      "outputs": [],
+      "source": [
+        "%%writefile instances.json\n",
+        "{\n",
+        "    \"instances\": [\n",
+        "        [214.0, \"360\", \"Rural\", 2.13, 2.21, 0.0, 0.0, 2.31, 2.01, 0.0, 0.0, 0.0, 0.0],\n",
+        "        [213.0, \"360\", \"Semiurban\", 2.03, 2.11, 0.0, 0.0, 2.13, 2.02, 0.0, 0.0, 0.0, 0.0]\n",
+        "    ]\n",
+        "}"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "b7cbfec4537d"
+      },
+      "source": [
+        "Use `curl` to send the prediction request to the Vertex AI endpoint. The response contains the predicted label (`0 == not eligible`, `1 == eligible`) for each instance sent in the request payload."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "b1617d6e8a3d"
+      },
+      "outputs": [],
+      "source": [
+        "ENDPOINT_ID=!(gcloud ai endpoints list \\\n",
+        "              --region={REGION} \\\n",
+        "              --filter=display_name={MODEL_NAME} \\\n",
+        "              --format='value(name)')\n",
+        "\n",
+        "!curl -X POST \\\n",
+        "   -H \"Authorization: Bearer $(gcloud auth print-access-token)\" \\\n",
+        "   -H \"Content-Type: application/json\" \\\n",
+        "   https://{REGION}-aiplatform.googleapis.com/v1/projects/{PROJECT_ID}/locations/us-central1/endpoints/{ENDPOINT_ID[-1]}:predict \\\n",
+        "   -d \"@instances.json\""
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -2352,8 +2704,14 @@
         "# Delete pipeline\n",
         "pipeline.delete()\n",
         "\n",
+        "# Delete endpoints\n",
+        "endpoint_list = vertex_ai.Endpoint.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
+        "for endpoint in endpoint_list:\n",
+        "    endpoint.undeploy_all()\n",
+        "    endpoint.delete()\n",
+        "\n",
         "# Delete model\n",
-        "model_list = vertex_ai.TabularDataset.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
+        "model_list = vertex_ai.Model.list(filter=f'display_name=\"{MODEL_NAME}\"')\n",
         "for model in model_list:\n",
         "    model.delete()\n",
         "\n",

notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb

@@ -64,30 +64,6 @@
         "This notebook shows how to use [the Kubeflow Pipelines (KFP) SDK](https://www.kubeflow.org/docs/components/pipelines/) to build [Vertex AI Pipelines](https://cloud.google.com/vertex-ai/docs/pipelines) that generate model metrics and metrics visualizations, and comparing pipeline runs."
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:wine,lcn,sklearn"
-      },
-      "source": [
-        "### Datasets\n",
-        "\n",
-        "The dataset used for this tutorial is the [Wine dataset](https://archive.ics.uci.edu/ml/datasets/wine) from [Scikit-learn builtin datasets](https://scikit-learn.org/stable/datasets.html).\n",
-        "\n",
-        "The dataset predicts the origin of a wine."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "dataset:iris,lcn,sklearn"
-      },
-      "source": [
-        "The dataset used for this tutorial is the [Iris dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html) from [Scikit-learn builtin datasets](https://scikit-learn.org/stable/datasets.html).\n",
-        "\n",
-        "The dataset predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -112,6 +88,30 @@
         "- Compare metrics across pipeline runs"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:wine,lcn,sklearn"
+      },
+      "source": [
+        "### Datasets\n",
+        "\n",
+        "The dataset used for this tutorial is the [Wine dataset](https://archive.ics.uci.edu/ml/datasets/wine) from [Scikit-learn builtin datasets](https://scikit-learn.org/stable/datasets.html).\n",
+        "\n",
+        "The dataset predicts the origin of a wine."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dataset:iris,lcn,sklearn"
+      },
+      "source": [
+        "The dataset used for this tutorial is the [Iris dataset](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html) from [Scikit-learn builtin datasets](https://scikit-learn.org/stable/datasets.html).\n",
+        "\n",
+        "The dataset predicts the type of Iris flower species from a class of three species: setosa, virginica, or versicolor."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -202,7 +202,7 @@
         "! pip3 install {USER_FLAG} kfp google-cloud-pipeline-components --upgrade -q\n",
         "\n",
         "if os.getenv(\"IS_TESTING\"):\n",
-        "    ! pip3 install --upgrade matplotlib $USER_FLAG"
+        "    ! pip3 install --upgrade matplotlib $USER_FLAG -q"
       ]
     },
     {
@@ -240,6 +240,8 @@
         "id": "check_versions"
       },
       "source": [
+        "### KFP SDK version\n",
+        "\n",
         "Check the versions of the packages you installed.  The KFP SDK version should be >=1.6."
       ]
     },
@@ -349,7 +351,10 @@
       },
       "outputs": [],
       "source": [
-        "REGION = \"us-central1\"  # @param {type: \"string\"}"
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
       ]
     },
     {
@@ -358,9 +363,9 @@
         "id": "timestamp"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append the timestamp onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -371,9 +376,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
+        "\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()"
       ]
     },
     {
@@ -384,7 +396,7 @@
       "source": [
         "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated. Skip this step.\n",
+        "**If you are using Vertex AI Workbench Notebook**, your environment is already authenticated.\n",
         "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
         "when prompted to authenticate your account via oAuth.\n",
@@ -479,7 +491,7 @@
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + TIMESTAMP\n",
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
         "    BUCKET_URI = \"gs://\" + BUCKET_NAME"
       ]
     },
@@ -553,6 +565,10 @@
       },
       "outputs": [],
       "source": [
+        "import sys\n",
+        "\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "\n",
         "if (\n",
         "    SERVICE_ACCOUNT == \"\"\n",
         "    or SERVICE_ACCOUNT is None\n",
@@ -912,12 +928,12 @@
       },
       "outputs": [],
       "source": [
-        "DISPLAY_NAME = \"iris_\" + TIMESTAMP\n",
+        "DISPLAY_NAME = \"iris_\" + UUID\n",
         "\n",
         "job = aip.PipelineJob(\n",
         "    display_name=DISPLAY_NAME,\n",
         "    template_path=\"tabular classification_pipeline.json\".replace(\" \", \"_\"),\n",
-        "    job_id=f\"tabular classification-v2{TIMESTAMP}-1\".replace(\" \", \"\"),\n",
+        "    job_id=f\"tabular classification-v2{UUID}-1\".replace(\" \", \"\"),\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         "    parameter_values={\"seed\": 7, \"splits\": 10},\n",
         ")\n",
@@ -950,7 +966,18 @@
         "\n",
         "Next, generate another pipeline run that uses a different `seed` and `split` for the `iris_logregression` step.\n",
         "\n",
-        "Submit the new pipeline run:"
+        "Submit the new pipeline run:\n",
+        "\n",
+        "\n",
+        "**pipeline_root :**   Specify a Cloud Storage URI that your pipelines service account can access. The artifacts of your pipeline runs are stored within the pipeline root. \n",
+        "\n",
+        "**display_name :**  The name of the pipeline, this will show up in the Google Cloud console. \n",
+        "\n",
+        "**parameter_values :**  The pipeline parameters to pass to this run. For example, create a dict() with the parameter names as the dictionary keys and the parameter values as the dictionary values. \n",
+        "\n",
+        "**job_id :**  A unique identifier for this pipeline run. If the job ID is not specified, Vertex AI Pipelines creates a job ID for you using the pipeline name and the timestamp of when the pipeline run was started. \n",
+        "\n",
+        "**template_path :**  complete pipeline path"
       ]
     },
     {
@@ -962,9 +989,9 @@
       "outputs": [],
       "source": [
         "job = aip.PipelineJob(\n",
-        "    display_name=\"iris_\" + TIMESTAMP,\n",
+        "    display_name=\"iris_\" + UUID,\n",
         "    template_path=\"tabular classification_pipeline.json\".replace(\" \", \"_\"),\n",
-        "    job_id=f\"tabular classification-pipeline-v2{TIMESTAMP}-2\".replace(\" \", \"\"),\n",
+        "    job_id=f\"tabular classification-pipeline-v2{UUID}-2\".replace(\" \", \"\"),\n",
         "    pipeline_root=PIPELINE_ROOT,\n",
         "    parameter_values={\"seed\": 5, \"splits\": 7},\n",
         ")\n",
@@ -1081,16 +1108,7 @@
         "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 -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:\n",
-        "\n",
-        "- Dataset\n",
-        "- Pipeline\n",
-        "- Model\n",
-        "- Endpoint\n",
-        "- Batch Job\n",
-        "- Custom Job\n",
-        "- Hyperparameter Tuning Job\n",
-        "- Cloud Storage Bucket"
+        "Otherwise, you can delete the individual resources you created in this tutorial -- *Note:* this is auto-generated and not all resources may be applicable for this tutorial:"
       ]
     },
     {
@@ -1101,94 +1119,9 @@
       },
       "outputs": [],
       "source": [
-        "delete_dataset = True\n",
-        "delete_pipeline = True\n",
-        "delete_model = True\n",
-        "delete_endpoint = True\n",
-        "delete_batchjob = True\n",
-        "delete_customjob = True\n",
-        "delete_hptjob = True\n",
-        "delete_bucket = True\n",
-        "\n",
-        "try:\n",
-        "    if delete_model and \"DISPLAY_NAME\" in globals():\n",
-        "        models = aip.Model.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        model = models[0]\n",
-        "        aip.Model.delete(model)\n",
-        "        print(\"Deleted model:\", model)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
-        "\n",
-        "try:\n",
-        "    if delete_endpoint and \"DISPLAY_NAME\" in globals():\n",
-        "        endpoints = aip.Endpoint.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}_endpoint\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        endpoint = endpoints[0]\n",
-        "        endpoint.undeploy_all()\n",
-        "        aip.Endpoint.delete(endpoint.resource_name)\n",
-        "        print(\"Deleted endpoint:\", endpoint)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
-        "\n",
-        "if delete_dataset and \"DISPLAY_NAME\" in globals():\n",
-        "    if \"tabular\" == \"tabular\":\n",
-        "        try:\n",
-        "            datasets = aip.TabularDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.TabularDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "    if \"tabular\" == \"image\":\n",
-        "        try:\n",
-        "            datasets = aip.ImageDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.ImageDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "    if \"tabular\" == \"text\":\n",
-        "        try:\n",
-        "            datasets = aip.TextDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.TextDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "    if \"tabular\" == \"video\":\n",
-        "        try:\n",
-        "            datasets = aip.VideoDataset.list(\n",
-        "                filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "            )\n",
-        "            dataset = datasets[0]\n",
-        "            aip.VideoDataset.delete(dataset.resource_name)\n",
-        "            print(\"Deleted dataset:\", dataset)\n",
-        "        except Exception as e:\n",
-        "            print(e)\n",
-        "\n",
-        "try:\n",
-        "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",
-        "        pipelines = aip.PipelineJob.list(\n",
-        "            filter=f\"display_name={DISPLAY_NAME}\", order_by=\"create_time\"\n",
-        "        )\n",
-        "        pipeline = pipelines[0]\n",
-        "        aip.PipelineJob.delete(pipeline.resource_name)\n",
-        "        print(\"Deleted pipeline:\", pipeline)\n",
-        "except Exception as e:\n",
-        "    print(e)\n",
+        "delete_bucket = False\n",
         "\n",
+        "job.delete()\n",
         "if delete_bucket or os.getenv(\"IS_TESTING\"):\n",
         "    ! gsutil rm -r $BUCKET_URI"
       ]

notebooks/official/pipelines/pipelines_intro_kfp.ipynb

@@ -81,7 +81,6 @@
         "The steps performed include:\n",
         "\n",
         "- Define and compile a `Vertex AI` pipeline.\n",
-        "- Schedule a recurring pipeline run.\n",
         "- Specify which service account to use for a pipeline run."
       ]
     },
@@ -97,13 +96,9 @@
         "\n",
         "* Vertex AI\n",
         "* Cloud Storage\n",
-        "* Cloud Functions\n",
-        "* Cloud Scheduler\n",
         "\n",
         "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
         "[Cloud Storage pricing](https://cloud.google.com/storage/pricing),\n",
-        "[Cloud Functions pricing](ttps://cloud.google.com/functions/pricing), and\n",
-        "[Clould Scheduler pricing]((https://cloud.google.com/scheduler/pricing)),\n",
         "and use the [Pricing\n",
         "Calculator](https://cloud.google.com/products/calculator/)\n",
         "to generate a cost estimate based on your projected usage."
@@ -926,69 +921,6 @@
         "job.delete()"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "schedule_pipeline_run"
-      },
-      "source": [
-        "## Recurring pipeline runs: create a scheduled pipeline job\n",
-        "\n",
-        "This section shows how to create a **scheduled pipeline job**.  You do this using the pipeline you already defined.\n",
-        "\n",
-        "Under the hood, the scheduled jobs are supported by the Cloud Scheduler and a Cloud Functions function.  Check first that the APIs for both of these services are enabled.\n",
-        "You will need to first enable the [enable the Cloud Scheduler API](http://console.cloud.google.com/apis/library/cloudscheduler.googleapis.com) and the [Cloud Functions and Cloud Build APIs](https://console.cloud.google.com/flows/enableapi?apiid=cloudfunctions,cloudbuild.googleapis.com) if you have not already done so.\n",
-        "Note:you need to [create an App Engine app for your project](https://cloud.google.com/scheduler/docs/quickstart) if one does not already exist.\n",
-        "\n",
-        "\n",
-        "See the [Cloud Scheduler](https://cloud.google.com/scheduler/docs/configuring/cron-job-schedules) documentation for more on the cron syntax.\n",
-        "\n",
-        "Create a scheduled pipeline job, passing as an argument the job specification file that you compiled above.\n",
-        "\n",
-        "*Note:* You can pass a `parameter_values` dict that specifies the pipeline input parameters you want to use."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "Ty5hDoNX2Ou8"
-      },
-      "outputs": [],
-      "source": [
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    from kfp.v2.google.client import AIPlatformClient  # noqa: F811\n",
-        "\n",
-        "    api_client = AIPlatformClient(project_id=PROJECT_ID, region=REGION)\n",
-        "\n",
-        "    # adjust time zone and cron schedule as necessary\n",
-        "    response = api_client.create_schedule_from_job_spec(\n",
-        "        job_spec_path=\"intro_pipeline.json\",\n",
-        "        schedule=\"2 * * * *\",\n",
-        "        time_zone=\"America/Los_Angeles\",  # change this as necessary\n",
-        "        parameter_values={\"text\": \"Hello world!\"},\n",
-        "        # pipeline_root=PIPELINE_ROOT  # this argument is necessary if you did not specify PIPELINE_ROOT as part of the pipeline definition.\n",
-        "    )"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "J8AP1viy2Ou8"
-      },
-      "source": [
-        "Once the scheduled job is created, you can see it listed in the [Cloud Scheduler](https://console.cloud.google.com/cloudscheduler/) panel in the Console.\n",
-        "\n",
-        "<a href=\"https://storage.googleapis.com/amy-jo/images/kf-pls/pipelines_scheduler.png\" target=\"_blank\"><img src=\"https://storage.googleapis.com/amy-jo/images/kf-pls/pipelines_scheduler.png\" width=\"95%\"/></a>\n",
-        "\n",
-        "You can test the setup from the Cloud Scheduler panel by clicking 'RUN NOW'.\n",
-        "\n",
-        "> **Note**: The implementation is using a Cloud Functions function, which you can see listed in the [Cloud Functions](https://console.cloud.google.com/functions/list) panel in the console as `templated_http_request-v1`.\n",
-        "Don't delete this function, as it will prevent the Cloud Scheduler jobs from actually kicking off the pipeline run.  If you do delete it, create a new scheduled job in order to recreate the function.\n",
-        "\n",
-        "When you're done experimenting, you probably want to **PAUSE** your scheduled job from the Cloud Scheduler panel, so that the recurrent jobs do not keep running."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1222,7 +1154,7 @@
       "outputs": [],
       "source": [
         "delete_pipeline = True\n",
-        "delete_bucket = True\n",
+        "delete_bucket = False\n",
         "\n",
         "try:\n",
         "    if delete_pipeline and \"DISPLAY_NAME\" in globals():\n",

notebooks/official/workbench/fraud_detection/fraud-detection-model.ipynb

@@ -1,14 +1,5 @@
 {
   "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "c4b363e1330b"
-      },
-      "source": [
-        "# Build a fraud detection model on Vertex AI"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -38,6 +29,8 @@
         "id": "05c670d35496"
       },
       "source": [
+        "# Build a fraud detection model on Vertex AI\n",
+        "\n",
         "<table align=\"left\">\n",
         "\n",
         "  <td>\n",
@@ -60,28 +53,6 @@
         "</table>"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4c5fb7f2090f"
-      },
-      "source": [
-        "## Table of contents\n",
-        "\n",
-        "* [Overview](#section-1)\n",
-        "* [Dataset](#section-2)\n",
-        "* [Objective](#section-3)\n",
-        "* [Costs](#section-4)\n",
-        "* [Analyze the dataset](#section-5)\n",
-        "* [Fit a random forest model](#section-6)\n",
-        "* [Analyzing results](#section-7)\n",
-        "* [Save the model to a Cloud Storagae path](#section-8)\n",
-        "* [Create a model in Vertex AI](#section-9)\n",
-        "* [Create an Endpoint](#section-10)  \n",
-        "* [What-If Tool ](#section-11)\n",
-        "* [Clean up](#section-12)"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -94,19 +65,6 @@
         "This tutorial shows you how to build, deploy, and analyze predictions from a simple [random forest](https://en.wikipedia.org/wiki/Random_forest) model using tools like scikit-learn, Vertex AI, and the [What-IF Tool (WIT)](https://cloud.google.com/ai-platform/prediction/docs/using-what-if-tool) on a synthetic fraud transaction dataset to solve a financial fraud detection problem.\n"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "9625185ccee9"
-      },
-      "source": [
-        "## Dataset\n",
-        "<a name=\"section-2\"></a>\n",
-        "\n",
-        "\n",
-        "The dataset used in this tutorial is publicly available at Kaggle. See [Synthetic Financial Datasets For Fraud Detection](https://www.kaggle.com/ealaxi/paysim1)."
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -118,6 +76,13 @@
         "\n",
         "This tutorial demonstrates data analysis and model-building using a synthetic financial dataset. The model is trained on identifying fraudulent cases among the transactions. Then, the trained model is deployed on a Vertex AI Endpoint and analyzed using the What-If Tool. The steps taken in this tutorial are as follows: \n",
         "\n",
+        "This tutorial uses the following Google Cloud ML services and resources:\n",
+        "\n",
+        "- Vertex AI Model\n",
+        "- Vertex AI Endpoint\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
         "- Installation of required libraries\n",
         "- Reading the dataset from a Cloud Storage bucket\n",
         "- Performing exploratory analysis on the dataset\n",
@@ -129,6 +94,19 @@
         "- Un-deploying the model and cleaning up the model resources"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3037523e7523"
+      },
+      "source": [
+        "## Dataset\n",
+        "<a name=\"section-2\"></a>\n",
+        "\n",
+        "\n",
+        "The dataset used in this tutorial is publicly available at Kaggle. See [Synthetic Financial Datasets For Fraud Detection](https://www.kaggle.com/ealaxi/paysim1)."
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -154,21 +132,15 @@
     {
       "cell_type": "markdown",
       "metadata": {
-        "id": "1ba37fa1511f"
+        "id": "cd1bc75a1cb2"
       },
       "source": [
         "### Set up your local development environment\n",
         "\n",
         "**If you are using Colab or Google Cloud Notebooks**, your environment already meets\n",
-        "all the requirements to run this notebook. You can skip this step."
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "cd1bc75a1cb2"
-      },
-      "source": [
+        "all the requirements to run this notebook. You can skip this step.\n",
+        "\n",
+        "\n",
         "**Otherwise**, make sure your environment meets this notebook's requirements.\n",
         "You need the following:\n",
         "\n",
@@ -211,142 +183,44 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
         "id": "172533a994ad"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "flake8 4.0.1 requires importlib-metadata<4.3; python_version < \"3.8\", but you have importlib-metadata 4.12.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
       "source": [
         "import os\n",
         "\n",
-        "import google.auth\n",
+        "# The Vertex AI Workbench Notebook product has specific requirements\n",
+        "IS_WORKBENCH_NOTEBOOK = os.getenv(\"DL_ANACONDA_HOME\")\n",
+        "IS_USER_MANAGED_WORKBENCH_NOTEBOOK = os.path.exists(\n",
+        "    \"/opt/deeplearning/metadata/env_version\"\n",
+        ")\n",
         "\n",
+        "# Vertex AI Notebook requires dependencies to be installed with '--user'\n",
         "USER_FLAG = \"\"\n",
-        "# Google Cloud Notebook requires dependencies to be installed with '--user'\n",
-        "if \"default\" in dir(google.auth):\n",
-        "    USER_FLAG = \"--user\""
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "a465cf9367de"
-      },
-      "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 notebook environment to install the Vertex SDK for Python:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "6380f7ee5f54"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} --upgrade google-cloud-aiplatform"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "1969a1cc46cf"
-      },
-      "source": [
-        "Run the following command in your notebook environment to install witwidget:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "8b10e59b0911"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} witwidget"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "4099ce79705a"
-      },
-      "source": [
-        "Run the following command in your notebook environment to install joblib:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "1e56d524753a"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} joblib"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b87ee3041f7d"
-      },
-      "source": [
-        "Run the following command in your notebook environment to install scikit-learn:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "c3ebecd9bd72"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} scikit-learn"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "b624b5163531"
-      },
-      "source": [
-        "Run the following command in your notebook environment to install fsspec:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "79c7a64b04de"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} fsspec"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "5593090dcf0a"
-      },
-      "source": [
-        "Run the following command in your notebook environment to install gcsfs:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "7bf981bc5bf6"
-      },
-      "outputs": [],
-      "source": [
-        "! pip install {USER_FLAG} gcsfs"
+        "# Install the latest version of the Vertex AI client library.\n",
+        "! pip3 install --upgrade google-cloud-aiplatform {USER_FLAG} -q\n",
+        "\n",
+        "# Install additional libraries\n",
+        "! pip3 install {USER_FLAG} witwidget -q\n",
+        "! pip3 install {USER_FLAG} joblib -q\n",
+        "! pip3 install {USER_FLAG} scikit-learn -q\n",
+        "! pip3 install {USER_FLAG} fsspec -q\n",
+        "! pip3 install {USER_FLAG} gcsfs -q"
       ]
     },
     {
@@ -378,21 +252,14 @@
         "    app.kernel.do_shutdown(True)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "2d9b3731b3e0"
-      },
-      "source": [
-        "## Before you begin"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "7a5cb1df1ef7"
       },
       "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",
@@ -426,19 +293,26 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "b27f37ed1ccf"
+        "id": "dcdfccf50581"
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "\n",
-        "PROJECT_ID = \"\"\n",
-        "\n",
-        "# Get your Google Cloud project ID from gcloud\n",
-        "if not os.getenv(\"IS_TESTING\"):\n",
-        "    shell_output = !gcloud config list --format 'value(core.project)' 2>/dev/null\n",
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "5bf9979b96ff"
+      },
+      "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)"
+        "    print(\"Project ID:\", PROJECT_ID)"
       ]
     },
     {
@@ -450,18 +324,6 @@
         "Otherwise, set your project ID here."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "3dbdf6a5c539"
-      },
-      "outputs": [],
-      "source": [
-        "if PROJECT_ID == \"\" or PROJECT_ID is None:\n",
-        "    PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -473,15 +335,49 @@
         "! gcloud config set project $PROJECT_ID"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "264543a144ad"
+      },
+      "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. It is recommended 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)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "3281bedf6d3c"
+      },
+      "outputs": [],
+      "source": [
+        "REGION = \"[your-region]\"  # @param {type: \"string\"}\n",
+        "\n",
+        "if REGION == \"[your-region]\":\n",
+        "    REGION = \"us-central1\""
+      ]
+    },
     {
       "cell_type": "markdown",
       "metadata": {
         "id": "e663bd062c6f"
       },
       "source": [
-        "#### Timestamp\n",
+        "#### 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 timestamp for each instance session, and append it onto the name of resources you create in this tutorial."
+        "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."
       ]
     },
     {
@@ -492,21 +388,16 @@
       },
       "outputs": [],
       "source": [
-        "from datetime import datetime\n",
+        "import random\n",
+        "import string\n",
         "\n",
-        "TIMESTAMP = datetime.now().strftime(\"%Y%m%d%H%M%S\")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "0c7f603fcdcf"
-      },
-      "source": [
-        "### Authenticate your Google Cloud account\n",
         "\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already\n",
-        "authenticated. Skip this step."
+        "# Generate a uuid of length 8\n",
+        "def generate_uuid():\n",
+        "    return \"\".join(random.choices(string.ascii_lowercase + string.digits, k=8))\n",
+        "\n",
+        "\n",
+        "UUID = generate_uuid()"
       ]
     },
     {
@@ -515,6 +406,11 @@
         "id": "72bf8f7c9ab3"
       },
       "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "**If you are using Vertex AI Workbench Notebooks**, your environment is already\n",
+        "authenticated. Skip this step.\n",
+        "\n",
         "**If you are using Colab**, run the cell below and follow the instructions\n",
         "when prompted to authenticate your account via oAuth.\n",
         "\n",
@@ -547,19 +443,19 @@
       },
       "outputs": [],
       "source": [
-        "import os\n",
-        "import sys\n",
-        "\n",
         "# 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",
-        "# The Google Cloud Notebook product has specific requirements\n",
-        "IS_GOOGLE_CLOUD_NOTEBOOK = os.path.exists(\"/opt/deeplearning/metadata/env_version\")\n",
+        "import os\n",
+        "import sys\n",
         "\n",
-        "# If on Google Cloud Notebooks, then don't execute this code\n",
-        "if not IS_GOOGLE_CLOUD_NOTEBOOK:\n",
+        "# If on Vertex AI Workbench, then don't execute this code\n",
+        "IS_COLAB = \"google.colab\" in sys.modules\n",
+        "if not os.path.exists(\"/opt/deeplearning/metadata/env_version\") and not os.getenv(\n",
+        "    \"DL_ANACONDA_HOME\"\n",
+        "):\n",
         "    if \"google.colab\" in sys.modules:\n",
         "        from google.colab import auth as google_auth\n",
         "\n",
@@ -601,27 +497,25 @@
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "f56c52ba662c"
+        "id": "5e9a782f5608"
       },
       "outputs": [],
       "source": [
         "BUCKET_NAME = \"[your-bucket-name]\"  # @param {type:\"string\"}\n",
-        "REGION = \"[your-region]\"  # @param {type:\"string\"}"
+        "BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
       "cell_type": "code",
       "execution_count": null,
       "metadata": {
-        "id": "68d1f4908641"
+        "id": "6d0729c4ae94"
       },
       "outputs": [],
       "source": [
         "if BUCKET_NAME == \"\" or BUCKET_NAME is None or BUCKET_NAME == \"[your-bucket-name]\":\n",
-        "    BUCKET_NAME = PROJECT_ID + \"-vertex-ai-\" + TIMESTAMP\n",
-        "BUCKET_URI = f\"gs://{BUCKET_NAME}\"\n",
-        "if REGION == \"[your-region]\":\n",
-        "    REGION = \"us-central1\""
+        "    BUCKET_NAME = PROJECT_ID + \"aip-\" + UUID\n",
+        "    BUCKET_URI = f\"gs://{BUCKET_NAME}\""
       ]
     },
     {
@@ -697,6 +591,7 @@
         "import numpy as np\n",
         "import pandas as pd\n",
         "from google.cloud import aiplatform, storage\n",
+        "from IPython.display import display\n",
         "from sklearn.ensemble import RandomForestClassifier\n",
         "from sklearn.metrics import (average_precision_score, classification_report,\n",
         "                             confusion_matrix, f1_score)\n",
@@ -706,6 +601,15 @@
         "warnings.filterwarnings(\"ignore\")"
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fdcb614c716f"
+      },
+      "source": [
+        "## Load dataset"
+      ]
+    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -714,7 +618,6 @@
       },
       "outputs": [],
       "source": [
-        "# Load dataset\n",
         "df = pd.read_csv(\n",
         "    \"gs://cloud-samples-data/vertex-ai/managed_notebooks/fraud_detection/fraud_detection_data.csv\"\n",
         ")"
@@ -1031,6 +934,8 @@
         "\n",
         "# Upload the saved model file to Cloud Storage\n",
         "BLOB_PATH = \"[your-blob-path]\"\n",
+        "if BLOB_PATH == \"[your-blob-path]\":\n",
+        "    BLOB_PATH = \"fraud-detection-model-path\"\n",
         "BLOB_NAME = os.path.join(BLOB_PATH, FILE_NAME)\n",
         "\n",
         "bucket = storage.Client(PROJECT_ID).bucket(BUCKET_NAME)\n",
@@ -1057,6 +962,8 @@
       "outputs": [],
       "source": [
         "MODEL_DISPLAY_NAME = \"[your-model-display-name]\"\n",
+        "if MODEL_DISPLAY_NAME == \"[your-model-display-name]\":\n",
+        "    MODEL_DISPLAY_NAME = \"fraud-detection-model-display-name\"\n",
         "ARTIFACT_GCS_PATH = f\"{BUCKET_URI}/{BLOB_PATH}\"\n",
         "SERVING_CONTAINER_IMAGE_URI = (\n",
         "    \"us-docker.pkg.dev/vertex-ai/prediction/sklearn-cpu.1-0:latest\"\n",
@@ -1105,7 +1012,9 @@
       },
       "outputs": [],
       "source": [
-        "ENDPOINT_DISPLAY_NAME = \"[your-endpoint-display-name]\""
+        "ENDPOINT_DISPLAY_NAME = \"[your-endpoint-display-name]\"\n",
+        "if ENDPOINT_DISPLAY_NAME == \"[your-endpoint-display-name]\":\n",
+        "    ENDPOINT_DISPLAY_NAME = \"fraud-detection-endpoint\""
       ]
     },
     {
@@ -1143,6 +1052,8 @@
       "outputs": [],
       "source": [
         "DEPLOYED_MODEL_NAME = \"[your-deployed-model-name]\"\n",
+        "if DEPLOYED_MODEL_NAME == \"[your-deployed-model-name]\":\n",
+        "    DEPLOYED_MODEL_NAME = \"fraud-detection-deployed-model\"\n",
         "MACHINE_TYPE = \"n1-standard-2\""
       ]
     },
@@ -1224,34 +1135,33 @@
       },
       "outputs": [],
       "source": [
-        "# define target and labels\n",
-        "TARGET_FEATURE = \"isFraud\"\n",
-        "LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
+        "if not IS_COLAB:\n",
+        "    # define target and labels\n",
+        "    TARGET_FEATURE = \"isFraud\"\n",
+        "    LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
         "\n",
-        "# define the function to adjust the predictions\n",
+        "    # define the function to adjust the predictions\n",
         "\n",
+        "    def adjust_prediction(pred):\n",
+        "        return [1 - pred, pred]\n",
         "\n",
-        "def adjust_prediction(pred):\n",
-        "    return [1 - pred, pred]\n",
-        "\n",
-        "\n",
-        "# Combine the features and labels into one array for the What-If Tool\n",
-        "test_examples = np.hstack(\n",
-        "    (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
-        ")\n",
-        "\n",
-        "# Configure the WIT to run on the locally trained model\n",
-        "config_builder = (\n",
-        "    WitConfigBuilder(\n",
-        "        test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
+        "    # Combine the features and labels into one array for the What-If Tool\n",
+        "    test_examples = np.hstack(\n",
+        "        (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
         "    )\n",
-        "    .set_custom_predict_fn(forest.predict_proba)\n",
-        "    .set_target_feature(TARGET_FEATURE)\n",
-        "    .set_label_vocab(LABEL_VOCAB)\n",
-        ")\n",
         "\n",
-        "# display the WIT widget\n",
-        "WitWidget(config_builder, height=600)"
+        "    # Configure the WIT to run on the locally trained model\n",
+        "    config_builder = (\n",
+        "        WitConfigBuilder(\n",
+        "            test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
+        "        )\n",
+        "        .set_custom_predict_fn(forest.predict_proba)\n",
+        "        .set_target_feature(TARGET_FEATURE)\n",
+        "        .set_label_vocab(LABEL_VOCAB)\n",
+        "    )\n",
+        "\n",
+        "    # display the WIT widget\n",
+        "    display(WitWidget(config_builder, height=600))"
       ]
     },
     {
@@ -1271,36 +1181,35 @@
       },
       "outputs": [],
       "source": [
-        "# configure the target and class-labels\n",
-        "TARGET_FEATURE = \"isFraud\"\n",
-        "LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
+        "if not IS_COLAB:\n",
+        "    # configure the target and class-labels\n",
+        "    TARGET_FEATURE = \"isFraud\"\n",
+        "    LABEL_VOCAB = [\"not-fraud\", \"fraud\"]\n",
         "\n",
-        "# function to return predictions from the deployed Model\n",
+        "    # function to return predictions from the deployed Model\n",
         "\n",
+        "    def endpoint_predict_sample(instances: list):\n",
+        "        prediction = endpoint.predict(instances=instances)\n",
+        "        preds = [[1 - i, i] for i in prediction.predictions]\n",
+        "        return preds\n",
         "\n",
-        "def endpoint_predict_sample(instances: list):\n",
-        "    prediction = endpoint.predict(instances=instances)\n",
-        "    preds = [[1 - i, i] for i in prediction.predictions]\n",
-        "    return preds\n",
-        "\n",
-        "\n",
-        "# Combine the features and labels into one array for the What-If Tool\n",
-        "test_examples = np.hstack(\n",
-        "    (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
-        ")\n",
-        "\n",
-        "# Configure the WIT with the prediction function\n",
-        "config_builder = (\n",
-        "    WitConfigBuilder(\n",
-        "        test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
+        "    # Combine the features and labels into one array for the What-If Tool\n",
+        "    test_examples = np.hstack(\n",
+        "        (test_samples_X.to_numpy(), test_samples_y.to_numpy().reshape(-1, 1))\n",
         "    )\n",
-        "    .set_custom_predict_fn(endpoint_predict_sample)\n",
-        "    .set_target_feature(TARGET_FEATURE)\n",
-        "    .set_label_vocab(LABEL_VOCAB)\n",
-        ")\n",
         "\n",
-        "# run the WIT-widget\n",
-        "WitWidget(config_builder, height=400)"
+        "    # Configure the WIT with the prediction function\n",
+        "    config_builder = (\n",
+        "        WitConfigBuilder(\n",
+        "            test_examples.tolist(), test_samples_X.columns.tolist() + [\"isFraud\"]\n",
+        "        )\n",
+        "        .set_custom_predict_fn(endpoint_predict_sample)\n",
+        "        .set_target_feature(TARGET_FEATURE)\n",
+        "        .set_label_vocab(LABEL_VOCAB)\n",
+        "    )\n",
+        "\n",
+        "    # run the WIT-widget\n",
+        "    display(WitWidget(config_builder, height=400))"
       ]
     },
     {