Vertex ML Metadata notebook tutorials

This document contains a list of available Vertex ML Metadata notebook tutorials. These end-to-end tutorials help you get started using Vertex ML Metadata and can give you ideas for how to implement a specific project.

There are many environments in which you can host notebooks. You can:

  • Run them in the cloud using a service like Colaboratory (Colab) or Vertex AI Workbench.
  • Download them from GitHub and run them on your local machine.
  • Download them from GitHub and run them on a Jupyter or JupyterLab server in your local network.

Running a notebook in Colab is a way to get started quickly.

To open a notebook tutorial in Colab, click the Colab link in the notebook list. Colab creates a VM instance with all needed dependencies, launches the Colab environment, and loads the notebook.

You can also run the notebook using user-managed notebooks. When you create a user-managed notebooks instance with Vertex AI Workbench, you have full control over the hosting VM. You can specify the configuration and environment of the hosting VM.

To open a notebook tutorial in a Vertex AI Workbench instance:

  1. Click the Vertex AI Workbench link in the notebook list. The link opens the Vertex AI Workbench console.
  2. In the Deploy to notebook screen, type a name for your new Vertex AI Workbench instance and click Create.
  3. In the Ready to open notebook dialog that appears after the instance starts, click Open.
  4. On the Confirm deployment to notebook server page, select Confirm.
  5. Before running the notebook, select Kernel > Restart Kernel and Clear all Outputs.

List of notebooks

  • Select a service
  • AutoML
  • BigQuery
  • BigQuery ML
  • Custom training
  • Image
  • Ray on Vertex AI
  • Tabular
  • Text
  • Vector Search
  • Vertex AI Experiments
  • Vertex AI Feature Store
  • Vertex AI model evaluation
  • Vertex AI Model Monitoring
  • Vertex AI Model Registry
  • Vertex AI Pipelines
  • Vertex AI Prediction
  • Vertex AI TensorBoard
  • Vertex AI Vizier
  • Vertex AI Workbench
  • Vertex Explainable AI
  • Vertex ML Metadata
  • Video

Services Description Open in
Vertex AI Experiments
Vertex ML Metadata
Build Vertex AI Experiment lineage for custom training.
Learn how to integrate preprocessing code in a Vertex AI experiments. Learn more about Vertex AI Experiments. Learn more about Vertex ML Metadata.
  • Execute module for preprocessing data
  • Create a dataset artifact
  • Log parameters
  • Execute module for training the model
  • Log parameters
  • Create model artifact
  • Assign tracking lineage to dataset, model and parameters
Colab
Colab Enterprise
GitHub
Vertex AI Workbench
Vertex AI Experiments
Vertex ML Metadata
Custom training
Get started with Vertex AI Experiments.
Learn how to use Vertex AI Experiments when training with Vertex AI. Learn more about Vertex AI Experiments. Learn more about Vertex ML Metadata. Learn more about Custom training.
  • Local (notebook) training
  • Create an experiment.
  • Create a first run in the experiment.
  • Log parameters and metrics.
  • Create artifact lineage.
  • Visualize the experiment results.
  • Execute a second run.
  • Compare the two runs in the experiment.
  • Cloud (Vertex AI) training
  • Within the training script
Colab
Colab Enterprise
GitHub
Vertex AI Workbench
Vertex ML Metadata
Track parameters and metrics for custom training jobs.
Learn how to use Vertex AI SDK for Python to:
  • Track training parameters and prediction metrics for a custom training job.
  • Extract and perform analysis for all parameters and metrics within an Experiment.
Colab
Colab Enterprise
GitHub
Vertex AI Workbench
Vertex ML Metadata
Track parameters and metrics for locally trained models.
Learn how to use Vertex ML Metadata to track training parameters and evaluation metrics. Learn more about Vertex ML Metadata.
  • Track parameters and metrics for a locally trained model.
  • Extract and perform analysis for all parameters and metrics within an experiment.
Colab
Colab Enterprise
GitHub
Vertex AI Workbench
Vertex ML Metadata
Vertex AI Pipelines
Track artifacts and metrics across Vertex AI Pipelines runs using Vertex ML Metadata.
Learn how to track artifacts and metrics with Vertex ML Metadata in Vertex AI Pipeline runs. Learn more about Vertex ML Metadata. Learn more about Vertex AI Pipelines.
  • Use the Kubeflow Pipelines SDK to build an ML pipeline that runs on Vertex AI.
  • The pipeline creates a dataset, trains a scikitlearn model, and deploys the model to an endpoint.
  • Write custom pipeline components that generate artifacts and metadata.
  • Compare Vertex AI Pipeline runs, both in the Google Cloud console and programmatically.
  • Trace the lineage for pipelinegenerated artifacts.
  • Query your pipeline run metadata.
Colab
Colab Enterprise
GitHub
Vertex AI Workbench