Setup

Setup includes information about setting up a project for Vertex AI Feature Store (Legacy) and the required permissions for using Vertex AI Feature Store (Legacy).

Configure project

The following procedure describes how to create a new project and enable the Vertex AI API. This API is required to use Vertex AI Feature Store (Legacy). If you already have an existing project with the Vertex AI API enabled, you can use that project instead of creating a new project.

  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.

    Go to project selector

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

  4. Enable the Vertex AI API.

    Enable the API

    5.

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

    Go to project selector

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

  7. Enable the Vertex AI API.

    Enable the API

    8.

Vertex AI Feature Store (Legacy) service agent

In addition to user permissions, Vertex AI Feature Store (Legacy) acts on your behalf to perform operations such as accessing source data. To do so, Vertex AI Feature Store (Legacy) uses a service agent: service-PROJECT_NUMBER@gcp-sa-aiplatform.iam.gserviceaccount.com. By default, the service agent grants Vertex AI Feature Store (Legacy) access to source data in the same project where your featurestore is located. If the source data is in a different project from your featurestore, you must grant the service agent permission to access the project where the source data is located.

For more information, see Grant Vertex AI service agents access to other resources.

IAM permissions

Vertex AI admins have Vertex AI Feature Store (Legacy) admin privileges. If you require more granularity, Vertex AI Feature Store (Legacy) provides a set of predefined IAM roles. These roles provide different sets of permissions that are based on the following personas:

IT operations and DevOps
IT operations and DevOps manage Google Cloud resources and are responsible for creating featurestores and tuning their performance. You can use the featurestoreAdmin or featurestoreInstanceCreator role. The instance creator role lets you manage featurestores but prevents you from viewing data or writing data to the featurestores.
Data scientists and data engineers
Data scientists and data engineers create features and write data to featurestores. You can use the featurestoreResourceEditor role to manage entity types and features, and use the featurestoreDataWriter role to read and write feature values.
ML researchers and business analysts
ML researchers and business analysts search for features and export values for training models or making predictions; they don't need to create new features or write data. You can use the featurestoreResourceViewer role to search or browse for features and the featurestoreDataViewer role to read feature values.

For descriptions of each role and their associated permissions, see Predefined roles for Vertex AI.

Quotas and limits

Vertex AI Feature Store (Legacy) enforces quotas and limits to help you manage resources by setting your own usage limits and to protect the community of Google Cloud users by preventing unforeseen spikes in usage. To prevent you from hitting unplanned constraints, review Vertex AI Feature Store (Legacy) quotas on the Quotas and limits page. For example, Vertex AI Feature Store (Legacy) sets a quota on the number of online serving nodes and a quota on the number of online serving requests that you can make per minute.

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