Vertex AI Experiments is supported by the Agent Platform SDK for Python and
Google Cloud console. Vertex AI Experiments requires and depends on
[Vertex ML Metadata](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/ml-metadata/introduction.md.txt).

## Set up

1. Create a Service account. See [Create a service account with required permissions](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/experiments/tensorboard-training.md.txt#create_a_service_account_with_required_permissions).
2. Install the [Agent Platform SDK for Python.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/start/install-sdk.md.txt)
3. 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 ,
     - or, create your first experiment in the Agent Platform SDK for Python.  
       Optional: To configure with CMEK, see [Configure your project's metadata store.](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/ml-metadata/configure.md.txt)

## Supported Locations

The [Feature availability](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/general/locations.md.txt#americas_1) table lists the available locations for Vertex AI Experiments. When using
Agent Platform Pipelines or Vertex AI TensorBoard,
they must be in the same location as your Agent Platform experiment.

## What's next

- [Create an experiment](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/experiments/create-experiment.md.txt)

## Relevant notebook tutorials

1. [Compare trained and evaluated models](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/experiments/user-journey/uj-compare-models.md.txt)
2. [Model training with prebuilt data pre-processing code](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/experiments/user-journey/uj-model-training.md.txt)
3. [Compare pipeline runs](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/experiments/user-journey/uj-compare-pipeline-runs.md.txt)
4. [Autologging](https://docs.cloud.google.com/gemini-enterprise-agent-platform/machine-learning/experiments/user-journey/uj-autologging.md.txt)