model_garden/notebooks/official

Google Cloud Vertex AI Official Notebooks

The official notebooks are a collection of curated and non-curated notebooks authored by Google Cloud staff members. The curated notebooks are linked to in the Vertex AI online web documentation.

The official notebooks are organized by Google Cloud Vertex AI services.

Manifest of Curated Notebooks

AutoML

AutoML text classification model

In this tutorial, you learn how to use `AutoML` to train a text classification model.

This tutorial uses the following Google Cloud ML services:

• AutoML Training
• Vertex AI Model resource

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.

AutoML tabular forecasting model

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 Training
• Vertex AI Batch Prediction
• Vertex AI Model resource

The steps performed include:

• Create a Vertex AI Dataset resource.
• Train an AutoML tabular forecasting Model resource.
• Obtain the evaluation metrics for the Model resource.
• Make a batch prediction.

Vertex AI Training

Custom image classification model training and batch prediction

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.

This tutorial uses the following Google Cloud ML services:

• Vertex AI Training
• Vertex AI Batch Prediction
• Vertex AI Model resource

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.

Custom image classification model training and online prediction

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

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.
• Make a prediction.
• Undeploy the Model resource.

Vertex Explainable AI

AutoML tabular binary classification model with batch explanations

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.

This tutorial uses the following Google Cloud ML services:

• Vertex AI AutoML
• Vertex AI Batch Prediction
• Vertex Explainable AI
• Vertex AI Model resource

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.
• Make a batch prediction request with explainability.

AutoML tabular binary classification model with online explanations

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.

This tutorial uses the following Google Cloud ML services:

• Vertex AI AutoML
• Vertex AI Prediction
• Vertex Explainable AI
• Vertex AI Model resource
• Vertex AI Endpoint resource

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.

Custom tabular regression model with batch explanations

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

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.

Custom tabular regression model with online explanations

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.

Custom image classification model with batch explanations

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

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 explanation parameters as a Model resource.
• Make a batch prediction with explanations.

Custom image classification model with online explanations

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 Online 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.

Vertex Feature Store

Managing features in a feature store

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.

Vertex Model Monitoring

Monitoring drift detection in online serving

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.

Vertex ML Metadata

Tracking hyperparameters and metrics in custom training job

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.
• Extract and perform analysis for all parameters and metrics within an Experiment.

Tracking hyperparameters and metrics in locally trained job

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.

Vertex AI Pipelines

Creating Python function KFP components

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

AutoML image classification model pipeline

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

AutoML tabular classification model pipeline

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

AutoML tabular regression model pipeline

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

AutoML text classification model pipeline

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

Custom training and batch prediction using prebuilt components pipeline

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.

Custom training using prebuilt and custom components pipeline

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

Introduction to control flow in pipelines

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

Introduction to KFP components and pipelines

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.

Vertex AI Vizier

Using Vizier for multi-objective study