Merge branch 'main' into autoreview_13

notebooks/official/README.md

@@ -6,39 +6,29 @@ The official notebooks are organized by Google Cloud Vertex AI services.
 
 ## Manifest of Curated Notebooks
 
-### AutoML
+### AutoML  Text data 
 
-[AutoML text classification model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/automl-text-classification.ipynb)
 
-<blockquote>
-In this tutorial, you learn how to use `AutoML` to train a text classification model.
+[Create, train, and deploy an AutoML text classification model](official/automl/automl-text-classification.ipynb)
 
-This tutorial uses the following Google Cloud ML services:
-
-- `AutoML Training`
-- `Vertex AI Model resource`
+Learn how to use `AutoML` to train a text classification model.
 
 The steps performed include:
 
-- Create a `Vertex AI Dataset`
-- Train an `AutoML` text classification `Model` resource.
-- Obtain the evaluation metrics for the `Model` resource.
-- Create an `Endpoint` resource.
-- Deploy the `Model` resource to the `Endpoint` resource.
-- Make an online prediction.
-- Make a batch prediction.
-</blockquote>
+* Create a `Vertex AI Dataset`.
+* Train an `AutoML` text classification `Model` resource.
+* Obtain the evaluation metrics for the `Model` resource.
+* Create an `Endpoint` resource.
+* Deploy the `Model` resource to the `Endpoint` resource.
+* Make an online prediction
+* Make a batch prediction
 
-[AutoML tabular forecasting model](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
+### AutoML  Tabular data 
 
-<blockquote>
-In this tutorial, you create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK. 
 
-This tutorial uses the following Google Cloud ML services:
+[AutoML tabular forecasting model for batch prediction](official/automl/sdk_automl_tabular_forecasting_batch.ipynb)
 
-- `AutoML Training`
-- `Vertex AI Batch Prediction`
-- `Vertex AI Model` resource
+Learn how to create an `AutoML` tabular forecasting model from a Python script, and then do a batch prediction using the Vertex AI SDK.
 
 The steps performed include:
 
@@ -46,63 +36,266 @@ The steps performed include:
 - Train an `AutoML` tabular forecasting `Model` resource.
 - Obtain the evaluation metrics for the `Model` resource.
 - Make a batch prediction.
-</blockquote>
 
-### Vertex AI Training
+### BigQuery ML  Vertex AI Model Registry  Batch prediction 
 
-[Custom image classification model training and batch prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-batch.ipynb)
 
-<blockquote>
-In this tutorial, you 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.
+[Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions](official/model-registry/bqml-vertexai-model-registry.ipynb)
 
-This tutorial uses the following Google Cloud ML services:
+Learn how to use `Vertex AI Model Registry` with `BigQuery ML` and make batch predictions:
 
-- `Vertex AI Training`
-- `Vertex AI Batch Prediction`
-- `Vertex AI Model` resource
+The steps performed include:
+
+- Train a model with `BigQuery ML`
+- Upload the model to `Vertex AI Model Registry` 
+- Create a `Vertex AI Endpoint` resource
+- Deploy the `Model` resource to the `Endpoint` resource
+- Make `prediction` requests to the model endpoint
+- Run `batch prediction` job on the `Model` resource 
+
+
+### BigQuery ML  Vertex AI Model Registry  Online prediction 
+
+
+[Online prediction with BigQuery ML](official/bigquery_ml/bqml-online-prediction.ipynb)
+
+Learn how to train and deploy a churn prediction model for real-time inference, with the data in BigQuery and model trained using BigQuery ML, registered to Vertex AI Model Registry, and deployed to an endpoint on Vertex AI for online predictions.
+
+The steps performed include:
+
+- Using Python & SQL to query the public data in BigQuery
+- Preparing the data for modeling
+- Training a classification model using BigQuery ML and registering it to Vertex AI Model Registry
+- Inspecting the model on Vertex AI Model Registry
+- Deploying the model to an endpoint on Vertex AI
+- Making sample online predictions to the model endpoint
+
+
+### Custom Training 
+
+
+[Custom training and batch prediction](official/custom/sdk-custom-image-classification-batch.ipynb)
+
+Learn to use `Vertex AI Training` to create a custom trained model and use `Vertex AI Batch Prediction` to do a batch prediction on the trained model.
 
 The steps performed include:
 
 - Create a `Vertex AI` custom job for training a TensorFlow model.
 - Upload the trained model artifacts as a `Model` resource.
 - Make a batch prediction.
-</blockquote>
 
-[Custom image classification model training and online prediction](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/custom/sdk-custom-image-classification-online.ipynb)
+[Custom training and online prediction](official/custom/sdk-custom-image-classification-online.ipynb)
 
-<blockquote>
-In this tutorial, you 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. 
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Training`
-- `Vertex AI Prediction`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
+Learn to use `Vertex AI Training` to create a custom-trained model from a Python script in a Docker container, and learn to use `Vertex AI Prediction` to do a prediction on the deployed model by sending data.
 
 The steps performed include:
 
 - Create a `Vertex AI` custom job for training a TensorFlow model.
 - Upload the trained model artifacts to a `Model` resource.
 - Create a serving `Endpoint` resource.
-- Deploy the Model resource to a serving `Endpoint` resource.
+- Deploy the `Model` resource to a serving `Endpoint` resource.
 - Make a prediction.
 - Undeploy the `Model` resource.
-</blockquote>
 
-### Vertex Explainable AI
+### Tabular Data 
 
-[AutoML tabular binary classification model with batch explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_binary_classification_batch_explain.ipynb)
 
-<blockquote>
-In this tutorial, you 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.
+[Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS](official/automl/automl_forecasting_bqml_arima_plus_comparison.ipynb)
 
-This tutorial uses the following Google Cloud ML services:
+Learn how to create an BQML ARIMA_PLUS model using a training [Vertex AI Pipeline](https://cloud.
 
-- `Vertex AI AutoML`
-- `Vertex AI Batch Prediction`
-- `Vertex Explainable AI`
-- `Vertex AI Model` resource
+The steps performed are:
+
+- Train the BQML ARIMA_PLUS model.
+- View BQML model evaluation.
+- Make a batch prediction with the BQML model.
+- Create a Vertex AI `Dataset` resource.
+- Train the Vertex AI Forecasting model.
+- View the Model evaluation.
+- Make a batch prediction with the Model.
+
+
+### Vertex AI Experiments 
+
+
+[Compare pipeline runs with Vertex AI Experiments](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)
+
+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)
+
+Learn how to use Vertex AI Experiments to compare and evaluate model experiments.
+
+The steps performed include:
+
+- log the model parameters
+- log the loss and metrics on every epoch to TensorBoard
+- log the evaluation metrics
+
+
+### Vertex AI Feature Store 
+
+
+[Online and Batch predictions using Vertex AI Feature Store](official/feature_store/sdk-feature-store.ipynb)
+
+Learn how to use `Vertex AI Feature Store` to import feature data, and to access the feature data for both online serving and offline tasks, such as training.
+
+The steps performed include:
+
+- Create featurestore, entity type, and feature resources.
+- Import feature data into `Vertex AI Feature Store` resource.
+- Serve online prediction requests using the imported features.
+- Access imported features in offline jobs, such as training jobs.
+
+### Matching Engine 
+
+
+[Create Vertex AI Matching Engine index](official/matching_engine/sdk_matching_engine_for_indexing.ipynb)
+
+Learn how to create Approximate Nearest Neighbor (ANN) Index, query against indexes, and validate the performance of the index.
+
+The steps performed include:
+
+* Create ANN Index and Brute Force Index
+* Create an IndexEndpoint with VPC Network
+* Deploy ANN Index and Brute Force Index
+* Perform online query
+* Compute recall
+
+
+### Model Monitoring 
+
+
+[Vertex AI Model Monitoring with Explainable AI Feature Attributions](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.
+
+The steps performed include:
+
+- Upload a pre-trained model as a `Vertex AI Model` resource.
+- Create an `Vertex AI Endpoint` resource.
+- Deploy the `Model` resource to the `Endpoint` resource.
+- Configure the `Endpoint` resource for model monitoring.
+- Initialize the baseline distribution for model monitoring.
+- Generate synthetic prediction requests.
+- Understand how to interpret the statistics, visualizations, other data reported by the model monitoring feature.
+
+### Vertex AI Pipelines 
+
+
+[Lightweight Python function-based components, and component I/O](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.
+
+The steps performed include:
+
+- Build Python function-based KFP components.
+- Construct a KFP pipeline.
+- Pass *Artifacts* and *parameters* between components, both by path reference and by value.
+- Use the `kfp.dsl.importer` method.
+- 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)
+
+Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular classification model.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `Dataset` resource.
+    - Train an AutoML tabular 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`
+
+
+
+[AutoML tabular regression pipelines using google-cloud-pipeline-components](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.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Create a `Dataset` resource.
+    - Train an AutoML tabular regression `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)
+
+Learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build a custom model.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Train a custom model.
+    - Upload the trained model as a `Model` resource.
+    - Create an `Endpoint` resource.
+    - Deploy the `Model` resource to the `Endpoint` resource.
+    - Make a batch prediction request.
+
+
+
+[Pipeline control structures using the KFP SDK](official/pipelines/control_flow_kfp.ipynb)
+
+Learn how to use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.
+
+The steps performed include:
+
+- Create a KFP pipeline:
+    - Use control flow components
+- 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)
+
+Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
+
+The steps performed include:
+
+- Create KFP components:
+    - Generate ROC curve and confusion matrix visualizations for classification results
+    - Write metrics
+- Create KFP pipelines.
+- Execute KFP pipelines
+- Compare metrics across pipeline runs
+
+[Pipelines introduction for KFP](official/pipelines/pipelines_intro_kfp.ipynb)
+
+Learn how to use the KFP SDK to build pipelines that generate evaluation metrics.
+
+The steps performed include:
+
+- Define and compile a `Vertex AI` pipeline.
+- Specify which service account to use for a pipeline run.
+
+### 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)
+
+Learn to use `AutoML` to create a tabular binary classification model from a Python script, and then learn to use `Vertex AI Batch Prediction` to make predictions with explanations.
 
 The steps performed include:
 
@@ -110,89 +303,18 @@ The steps performed include:
 - Train an `AutoML` tabular binary classification model.
 - View the model evaluation metrics for the trained model.
 - Make a batch prediction request with explainability.
-</blockquote>
 
-[AutoML tabular binary classification model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_automl_tabular_classification_online_explain.ipynb)
 
-<blockquote>
-In this tutorial, you learn 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. 
+* Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.
 
-This tutorial uses the following Google Cloud ML services:
+* 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.
 
-- `Vertex AI AutoML`
-- `Vertex AI Prediction`
-- `Vertex Explainable AI`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
+### Vertex Explainable AI  Image data 
 
-The steps performed include:
 
-- Create a `Vertex AI 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.
-</blockquote>
+[Custom training image classification model for batch prediction with explainabilty](official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
 
-[Custom tabular regression model with batch explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_batch_explain.ipynb)
-
-<blockquote>
-In this tutorial, you 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. 
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Training`
-- `Vertex AI Batch Prediction`
-- `Vertex Explainable AI`
-- `Vertex AI Mode`l resource
-
-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.
-</blockquote>
-    
-[Custom tabular regression model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_tabular_regression_online_explain.ipynb)
-
-<blockquote>
-In this tutorial, you 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 Prediction` to make an online prediction request with explanations.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Training`
-- `Vertex AI Prediction`
-- `Vertex Explainable AI`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-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.
-</blockquote>
-    
-[Custom image classification model with batch explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_batch_explain.ipynb)
-
-<blockquote>
-In this tutorial, you 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. 
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Training`
-- `Vertex AI Batch Prediction`
-- `Vertex Explainable AI`
-- `Vertex AI Model` resource
+Learn to use `Vertex AI Training and Explainable AI` to create a custom image classification model with explanations, and then you learn to use `Vertex AI Batch Prediction` to make a batch prediction request with explanations.
 
 The steps performed include:
 
@@ -201,312 +323,17 @@ The steps performed include:
 - Set explanation parameters for when the model is deployed.
 - Upload the trained model artifacts and explanation parameters as a `Model` resource.
 - Make a batch prediction with explanations.
-</blockquote>
 
-[Custom image classification model with online explanations](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/explainable_ai/sdk_custom_image_classification_online_explain.ipynb)
+### Vertex ML Metadata 
 
-<blockquote>
-In this tutorial, you 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 Prediction` to make an online prediction request with explanations. 
 
-This tutorial uses the following Google Cloud ML services:
+[Track parameters and metrics for custom training jobs](official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
 
-- `Vertex AI Training`
-- `Vertex AI Online Prediction`
-- `Vertex Explainable AI`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
+Learn how to use Vertex AI SDK for Python to:
 
 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.
-</blockquote>
-
-### Vertex Feature Store
-
-[Managing features in a feature store](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/feature_store/gapic-feature-store.ipynb)
-
-<blockquote>
-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.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Feature Store`
-    
-The steps performed include:
-
-- Create featurestore, entity type, and feature resources.
-- Import feature data into `Vertex AI Feature Store` resource.
-- Serve online prediction requests using the imported features.
-- Access imported features in offline jobs, such as training jobs.
-
-</blockquote>
-
-### Vertex Model Monitoring
-
-[Monitoring drift detection in online serving](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/model_monitoring/model_monitoring.ipynb)
-
-<blockquote>
-In this notebook, you learn to use the `Vertex AI Model Monitoring` service to detect drift and anomalies in prediction requests from a deployed `Vertex AI Model` resource.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Model Monitoring`
-- `Vertex AI Prediction`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-The steps performed include:
-
-- Upload a pre-trained model as a `Vertex AI Model` resource.
-- Create an `Vertex AI Endpoint` resource.
-- Deploy the `Model` resource to the `Endpoint` resource.
-- Configure the `Endpoint` resource for model monitoring.
-- Generate synthetic prediction requests.
-- Understand how to interpret the statistics, visualizations, other data reported by the model monitoring feature.
-</blockquote>
-    
-### Vertex ML Metadata
-
-[Tracking hyperparameters and metrics in custom training job](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb)
-
-<blockquote>
-In this notebook, you learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex ML Metadata`
-- `Vertex AI Experiments`
-
-The steps performed include:
-
-- Track parameters and metrics for a `Vertex AI` custom trained model.
+- Track training parameters and prediction metrics for a custom training job.
 - Extract and perform analysis for all parameters and metrics within an Experiment.
-</blockquote>
-
-[Tracking hyperparameters and metrics in locally trained job](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb)
-
-<blockquote>
-In this notebook, you learn how to use `Vertex ML Metadata` to track training parameters and evaluation metrics.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex ML Metadata`
-- `Vertex AI Experiments`
-
-The steps performed include:
-
-- Track parameters and metrics for a locally trained model.
-- Extract and perform analysis for all parameters and metrics within an Experiment.
-</blockquote>
-    
-### Vertex AI Pipelines
-
-[Creating Python function KFP components](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb)
-
-<blockquote>
-In this tutorial, you 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.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-
-The steps performed include:
-
-- Build Python function-based KFP components.
-- Construct a KFP pipeline.
-    - Pass Artifacts and parameters between components, both by path reference and by value.
-    - Use the kfp.dsl.importer method.
-- Compile the KFP pipeline.
-- Execute the KFP pipeline using `Vertex AI Pipelines`
-</blockquote>
-    
-[AutoML image classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_images.ipynb)
-
-<blockquote>
-In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` image classification model.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-- `Google Cloud Pipeline Components`
-- `Vertex AutoML`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-The steps performed include:
-
-- Create a KFP pipeline:
-    - Create a `Vertex AI 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`
-</blockquote>
-    
-[AutoML tabular classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/automl_tabular_classification_beans.ipynb)
-
-<blockquote>
-In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an AutoML tabular classification model.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-- `Google Cloud Pipeline Components`
-- `Vertex AutoML`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-The steps performed include:
-
-- Create a KFP pipeline:
-    - Create a `Dataset` resource.
-    - Train an `AutoML` tabular 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`
-</blockquote>
-    
-[AutoML tabular regression model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_tabular.ipynb)
-
-<blockquote>
-In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` tabular regression model.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-- `Google Cloud Pipeline Components`
-- `Vertex AutoML`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-The steps performed include:
-
-- Create a KFP pipeline:
-    - Create a `Dataset` resource.
-    - Train an `AutoML` tabular regression `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`
-</blockquote>
-
-[AutoML text classification model pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb)
-
-<blockquote>
-In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build an `AutoML` text classification model.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-- `Google Cloud Pipeline Components`
-- `Vertex AutoML`
-- `Vertex AI Model` resource
-"- `Vertex AI Endpoint` resource
-
-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`
-</blockquote>
-
-[Custom training and batch prediction using prebuilt components pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/custom_model_training_and_batch_prediction.ipynb)
-
-<blockquote>
-In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build a custom model.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-- `Google Cloud Pipeline Components`
-- `Vertex AI Training`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-The steps performed include:
-
-- Create a KFP pipeline:
-    - Train a custom model.
-    - Upload the trained model as a `Model` resource.
-    - Create an `Endpoint` resource.
-- Deploy the `Model` resource to the `Endpoint` resource.
-- Make a batch prediction request.
-</blockquote>
-
-[Custom training using prebuilt and custom components pipeline](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/google_cloud_pipeline_components_model_train_upload_deploy.ipynb)
-
-<blockquote>
-In this tutorial, you learn to use `Vertex AI Pipelines` and `Google Cloud Pipeline Components` to build and deploy a custom model.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-- `Google Cloud Pipeline Components`
-- `Vertex AI Training`
-- `Vertex AI Model` resource
-- `Vertex AI Endpoint` resource
-
-The steps performed include:
-
-- Create a KFP pipeline:
-    - Train a custom model.
-    - Uploads the trained model as a `Model` resource.
-    - Creates an `Endpoint` resource.
-    - Deploys the `Model` resource to the `Endpoint` resource.
-- Compile the KFP pipeline.
-- Execute the KFP pipeline using `Vertex AI Pipelines`
-</blockquote>
-    
-[Introduction to control flow in pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/control_flow_kfp.ipynb)
-
-<blockquote>
-In this tutorial, you use the KFP SDK to build pipelines that use loops and conditionals, including nested examples.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-
-The steps performed include:
-
-- Create a KFP pipeline:
-    - Use control flow components
-- Compile the KFP pipeline.
-- Execute the KFP pipeline using `Vertex AI Pipelines`
-</blockquote>
-
-[Introduction to KFP components and pipelines](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/pipelines/pipelines_intro_kfp.ipynb)
-
-<blockquote>
-In this tutorial, you use the KFP SDK to build pipelines.
-
-This tutorial uses the following Google Cloud ML services:
-
-- `Vertex AI Pipelines`
-
-The steps performed include:
-
-- Define and compile a `Vertex AI` pipeline.
-- Schedule a recurring pipeline run.
-- Specify which service account to use for a pipeline run.
-</blockquote>
-
-### Vertex AI Vizier
-
-[Using Vizier for multi-objective study](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb)
-