Set up for Vertex AI Experiments

Vertex AI Experiments is supported by the Vertex AI SDK for Python and Google Cloud console. Vertex AI Experiments requires and depends on Vertex ML Metadata.

Set up

1. Sign in to your Google Cloud account. If you're new to Google Cloud, create an account to evaluate how our products perform in real-world scenarios. New customers also get $300 in free credits to run, test, and deploy workloads.

2. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

3. Make sure that billing is enabled for your Google Cloud project.

4. Enable the required API.

   Enable the API

5. In the Google Cloud console, on the project selector page, select or create a Google Cloud project.

   Note: If you don't plan to keep the resources that you create in this procedure, create a project instead of selecting an existing project. After you finish these steps, you can delete the project, removing all resources associated with the project.

   Go to project selector

6. Make sure that billing is enabled for your Google Cloud project.

7. Enable the required API.

   Enable the API

8. Create a Service account. See Create a service account with required permissions.
9. Install the Vertex AI SDK for Python.
10. Check for existence of the default Metadata Store in your project. (required)
    • To see if your project has the default Metadata Store, go to the Metadata page in the Google Cloud console.
    • If the default Metadata Store doesn't exist, it's created when
      • you run the first PipelineJob,
      • or, create your first experiment in the Vertex AI SDK for Python.
        Optional: To configure with CMEK, see Configure your project's metadata store.

Supported Locations

The Feature availability table lists the available locations for Vertex AI Experiments. When using Vertex AI Pipelines or Vertex AI TensorBoard, they must be in the same location as your Vertex AI experiment.

What's next

• Create an experiment

Relevant notebook tutorials

1. Compare trained and evaluated models
2. Model training with prebuilt data pre-processing code
3. Compare pipeline runs
4. Autologging