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 Text data

Create, train, and deploy an AutoML text classification model

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

AutoML Tabular data

AutoML tabular forecasting model for batch prediction

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:

• 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.

BigQuery ML Vertex AI Model Registry Batch prediction

Deploy BiqQuery ML Model on Vertex AI Model Registry and make predictions

Learn how to use Vertex AI Model Registry with BigQuery ML and make batch predictions:

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

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

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.

Custom training and online prediction

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

Tabular Data

Compare Vertex AI Forecasting and BigQuery ML ARIMA_PLUS

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

The steps performed are:

• Train the BQML ARIMA_PLUS model.
• View BQML model evaluation.
• Make a batch prediction with the BQML model.
• 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

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

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

Track parameters and metrics for locally trained models

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

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

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

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

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

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

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

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

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

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

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

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:

• 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.

• Prediction Service: Does an on-demand prediction for the entire set of instances (i.e., one or more data items) and returns the results in real-time.

• Batch Prediction Service: Does a queued (batch) prediction for the entire set of instances in the background and stores the results in a Cloud Storage bucket when ready.

Vertex Explainable AI Image data

Custom training image classification model for batch prediction with explainabilty

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:

• 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.

Vertex ML Metadata

Track parameters and metrics for custom training jobs

Learn how to use Vertex AI SDK for Python to:

The steps performed include:

• Track training parameters and prediction metrics for a custom training job.
• Extract and perform analysis for all parameters and metrics within an Experiment.