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

Configure project

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

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.

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

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the API

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

Roles required to select or create a project

• Select a project: Selecting a project doesn't require a specific IAM role—you can select any project that you've been granted a role on.
• Create a project: To create a project, you need the Project Creator role (roles/resourcemanager.projectCreator), which contains theresourcemanager.projects.create permission.Learn how to grant roles.

Go to project selector

Verify that billing is enabled for your Google Cloud project.

Enable the Vertex AI API.

Roles required to enable APIs

To enable APIs, you need the Service Usage Admin IAM role (roles/serviceusage.serviceUsageAdmin), which contains theserviceusage.services.enable permission.Learn how to grant roles.

Enable the API

Vertex AI Feature Store (Legacy) service agent

In addition to user permissions, Vertex AI Feature Store (Legacy) acts on yourbehalf 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) accessto source data in the same project where your featurestore is located. If thesource data is in a different project from your featurestore, you must grant theservice agent permission to access the project where the source data islocated.

For more information, seeGrant Vertex AI service agents access to otherresources.

IAM permissions

Vertex AI admins have Vertex AI Feature Store (Legacy) administratorprivileges. If you require more granularity, Vertex AI Feature Store (Legacy)provides a set of predefined IAM roles. These roles providedifferent 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 forcreating featurestores and tuning their performance. You can use thefeaturestoreAdmin orfeaturestoreInstanceCreator role. The instance creatorrole lets you manage featurestores but prevents you from viewing data orwriting data to the featurestores.

Data scientists and data engineers

Data scientists and data engineers create features and write data tofeaturestores. You can use thefeaturestoreResourceEditor role tomanage entity types and features, and use thefeaturestoreDataWriter role toread and write feature values.

ML researchers and business analysts

ML researchers and business analysts search for features and export values fortraining models or making predictions; they don't need to create new features orwrite data. You can use thefeaturestoreResourceViewer role to searchor browse for features and thefeaturestoreDataViewer role to read featurevalues.

For descriptions of each role and their associated permissions, seePredefined roles forVertex AI.

Quotas and limits

Vertex AI Feature Store (Legacy) enforces quotas and limits to help you manageresources by setting your own usage limits and to protect the community ofGoogle Cloud users by preventing unforeseen spikes in usage. To prevent you fromhitting unplanned constraints, review Vertex AI Feature Store (Legacy) quotason theQuotas and limits page. For example,Vertex AI Feature Store (Legacy) sets a quota on the number of online servingnodes and a quota on the number of online serving requests that you can make perminute.

What's next

• Learn aboutManage featurestores.
• Learn aboutbest practices for usingVertex AI Feature Store (Legacy).