Merge branch 'main' into autoreview_23

notebooks/official/README.md

@@ -9,7 +9,7 @@ The official notebooks are organized by Google Cloud Vertex AI services.
 ### AutoML  Text data 
 
 
-[Create, train, and deploy an AutoML text classification model](official/automl/automl-text-classification.ipynb)
+[Create, train, and deploy an AutoML text classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-text-classification.ipynb)
 
 Learn how to use `AutoML` to train a text classification model.
 
@@ -26,7 +26,7 @@ The steps performed include:
 ### AutoML  Tabular data 
 
 
-[AutoML tabular forecasting model for batch prediction](official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
+[AutoML tabular forecasting model for batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
 
 Learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.
 
@@ -40,7 +40,7 @@ The steps performed include:
 ### BigQuery ML  Vertex AI Model Registry  Batch prediction 
 
 
-[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](official/model-registry/bqml-vertexai-model-registry.ipynb)
+[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model-registry/bqml-vertexai-model-registry.ipynb)
 
 Learn how to use `Vertex AI Model Registry` with `BigQuery ML` and make batch predictions:
 
@@ -57,7 +57,7 @@ The steps performed include:
 ### BigQuery ML  Vertex AI Model Registry  Online prediction 
 
 
-[Online prediction with BigQuery ML](official/bigquery_ml/bqml-online-prediction.ipynb)
+[Online prediction with BigQuery ML](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/bigquery_ml/bqml-online-prediction.ipynb)
 
 Learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.
 
@@ -74,7 +74,7 @@ The steps performed include:
 ### Custom Training 
 
 
-[Custom training and batch prediction](official/custom/sdk-custom-image-classification-batch.ipynb)
+[Custom training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)
 
 Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
 
@@ -84,7 +84,7 @@ The steps performed include:
 - Upload the trained model artifacts as a `Model` resource.
 - Make a batch prediction.
 
-[Custom training and online prediction](official/custom/sdk-custom-image-classification-online.ipynb)
+[Custom training and online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.ipynb)
 
 Learn to use `Vertex AI Training` to create a custom-trained model from a Python script in a Docker container, and learn to use `Vertex AI Prediction` to do a prediction on the deployed model by sending data.
 
@@ -100,7 +100,7 @@ The steps performed include:
 ### Tabular Data 
 
 
-[Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)
+[Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)
 
 Learn how to create an BQML ARIMA_PLUS model using a training [Vertex AI Pipeline](https://cloud.
 
@@ -115,22 +115,34 @@ The steps performed are:
 - Make a batch prediction with the Model.
 
 
+### AutoML  Tabular Data 
+
+
+[AutoML Tabular Pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl_tabular_on_vertex_pipelines.ipynb)
+
+Learn how to create two regression models using [Vertex Pipelines](https://cloud.
+
+The steps performed are:
+
+- Create a training pipeline that reduces the search space from the default to save time.
+- Create a training pipeline that reuses the architecture search results from the previous pipeline to save time.
+
 ### Vertex AI Experiments 
 
 
-[Compare pipeline runs with Vertex AI Experiments](official/experiments/comparing_pipeline_runs.ipynb)
+[Compare pipeline runs with Vertex AI Experiments](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_pipeline_runs.ipynb)
 
 Learn how to use `Vertex AI Experiments` to log a pipeline job and compare different pipeline jobs.
 
 
 
-[Build Vertex AI Experiment lineage for custom training](official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb)
+[Build Vertex AI Experiment lineage for custom training](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/build_model_experimentation_lineage_with_prebuild_code.ipynb)
 
 Learn how to integrate preprocessing code in a Vertex AI experiments.
 
 
 
-[Track parameters and metrics for locally trained models](official/experiments/comparing_local_trained_models.ipynb)
+[Track parameters and metrics for locally trained models](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/experiments/comparing_local_trained_models.ipynb)
 
 Learn how to use Vertex AI Experiments to compare and evaluate model experiments.
 
@@ -144,7 +156,7 @@ The steps performed include:
 ### Vertex AI Feature Store 
 
 
-[Online and Batch predictions using Vertex AI Feature Store](official/feature_store/sdk-feature-store.ipynb)
+[Online and Batch predictions using Vertex AI Feature Store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/sdk-feature-store.ipynb)
 
 Learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.
 
@@ -158,7 +170,7 @@ The steps performed include:
 ### Matching Engine 
 
 
-[Create Vertex AI Matching Engine index](official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
+[Create Vertex AI Matching Engine index](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
 
 Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index.
 
@@ -174,7 +186,7 @@ The steps performed include:
 ### Model Monitoring 
 
 
-[Vertex AI Model Monitoring with Explainable AI Feature Attributions](official/model_monitoring/model_monitoring.ipynb)
+[Vertex AI Model Monitoring with Explainable AI Feature Attributions](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)
 
 Learn to use the `Vertex AI Model Monitoring` service to detect drift and anomalies in prediction requests from a deployed `Vertex AI Model` resource.
 
@@ -191,7 +203,7 @@ The steps performed include:
 ### Vertex AI Pipelines 
 
 
-[Lightweight Python function-based components, and component I/O](official/pipelines/lightweight_functions_component_io_kfp.ipynb)
+[Lightweight Python function-based components, and component I/O](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
 
 Learn to use the KFP SDK to build lightweight Python function-based components, and then you learn to use `Vertex AI Pipelines` to execute the pipeline.
 
@@ -204,10 +216,29 @@ The steps performed include:
 - Compile the KFP pipeline.
 - Execute the KFP pipeline using `Vertex AI Pipelines`
 
+### Vertex AI Pipelines Image data 
+
+
+[AutoML image classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
+
+Learn how to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `Dataset` resource.
+    - Train an AutoML image classification `Model` resource.
+    - Create an `Endpoint` resource.
+    - Deploys the `Model` resource to the `Endpoint` resource.
+- Compile the KFP pipeline.
+- Execute the KFP pipeline using `Vertex AI Pipelines`
+
+
+
 ### Vertex AI Pipelines  Tabular data 
 
 
-[AutoML Tabular pipelines using google-cloud-pipeline-components](official/pipelines/automl_tabular_classification_beans.ipynb)
+[AutoML Tabular pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular classification model.
 
@@ -223,7 +254,7 @@ The steps performed include:
 
 
 
-[AutoML tabular regression pipelines using google-cloud-pipeline-components](official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
+[AutoML tabular regression pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular regression model.
 
@@ -239,10 +270,29 @@ The steps performed include:
 
 
 
+### Vertex AI Pipelines  Text data 
+
+
+[AutoML text classification pipelines using google-cloud-pipeline-components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
+
+Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `Dataset` resource.
+    - Train an AutoML text classification `Model` resource.
+    - Create an `Endpoint` resource.
+    - Deploys the `Model` resource to the `Endpoint` resource.
+- Compile the KFP pipeline.
+- Execute the KFP pipeline using `Vertex AI Pipelines`
+
+
+
 ### Vertex AI Pipelines 
 
 
-[Custom training with pre-built Google Cloud Pipeline Components](official/pipelines/custom_model_training_and_batch_prediction.ipynb)
+[Custom training with pre-built Google Cloud Pipeline Components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)
 
 Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build a custom model.
 
@@ -257,7 +307,7 @@ The steps performed include:
 
 
 
-[Pipeline control structures using the KFP SDK](official/pipelines/control_flow_kfp.ipynb)
+[Pipeline control structures using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)
 
 Learn how to use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.
 
@@ -268,7 +318,7 @@ The steps performed include:
 - Compile the KFP pipeline.
 - Execute the KFP pipeline using `Vertex AI Pipelines`
 
-[Metrics visualization and run comparison using the KFP SDK](official/pipelines/metrics_viz_run_compare_kfp.ipynb)
+[Metrics visualization and run comparison using the KFP SDK](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/metrics_viz_run_compare_kfp.ipynb)
 
 Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
 
@@ -281,7 +331,7 @@ The steps performed include:
 - Execute KFP pipelines
 - Compare metrics across pipeline runs
 
-[Pipelines introduction for KFP](official/pipelines/pipelines_intro_kfp.ipynb)
+[Pipelines introduction for KFP](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
 
 Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
 
@@ -290,10 +340,19 @@ The steps performed include:
 - Define and compile a `Vertex AI` pipeline.
 - Specify which service account to use for a pipeline run.
 
+### Vertex AI Vizier 
+
+
+[Optimizing multiple objectives with Vertex AI Vizier](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb)
+
+Learn how to use `Vertex AI Vizier` to optimize a multi-objective study.
+
+
+
 ### Vertex Explainable AI  Tabular data 
 
 
-[AutoML training tabular binary classification model for batch explanation](official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb)
+[AutoML training tabular binary classification model for batch explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb)
 
 Learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Batch Prediction` to make predictions with explanations.
 
@@ -309,10 +368,24 @@ The steps performed include:
 
 * Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.
 
+[AutoML training tabular classification model for online explanation](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb)
+
+Learn how to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Online Prediction` to make online predictions with explanations.
+
+The steps performed include:
+
+- Create a `Vertex Dataset` resource.
+- Train an `AutoML` tabular binary classification model.
+- View the model evaluation metrics for the trained model.
+- Create a serving `Endpoint` resource.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make an online prediction request with explainability.
+- Undeploy the `Model` resource.
+
 ### Vertex Explainable AI  Image data 
 
 
-[Custom training image classification model for batch prediction with explainabilty](official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
+[Custom training image classification model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
 
 Learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
 
@@ -324,10 +397,40 @@ The steps performed include:
 - Upload the trained model artifacts and explanation parameters as a `Model` resource.
 - Make a batch prediction with explanations.
 
+[Custom training image classification model for online prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)
+
+Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Prediction` to make an online prediction request with explanations.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- View the model evaluation for the trained model.
+- Set explanation parameters for when the model is deployed.
+- Upload the trained model artifacts and explanations as a `Model` resource.
+- Create a serving `Endpoint` resource.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
+- Make a prediction with explanation.
+- Undeploy the `Model` resource.
+
+### Vertex Explainable AI  Tabular data 
+
+
+[Custom training tabular regression model for batch prediction with explainabilty](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
+
+Learn how to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
+
+The steps performed include:
+
+- Create a `Vertex AI` custom job for training a TensorFlow model.
+- View the model evaluation for the trained model.
+- Set explanation parameters for when the model is deployed.
+- Upload the trained model artifacts and explanations as a `Model` resource.
+- Make a batch prediction with explanations.
+
 ### Vertex ML Metadata 
 
 
-[Track parameters and metrics for custom training jobs](official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
+[Track parameters and metrics for custom training jobs](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
 
 Learn how to use Vertex AI SDK for Python to:
 
@@ -337,4 +440,5 @@ The steps performed include:
 
 
 
+