Centralized Feature Store for ML Models on GCP Datastore

Question

Pulumi · Accepted Answer

Creating a centralized feature store enables you to manage and serve features for machine learning models efficiently. The Google Cloud Platform (GCP) offers services that you can use to build a feature store system. To set this up, you can use Vertex AI's Feature Store service, which allows you to create, manage, and serve ML features at scale.

Below, you'll find a Pulumi program in Python that shows how you can create a feature store on GCP using the pulumi_gcp provider. The program sets up an AI Feature Store, which is a centralized repository for storing and retrieving feature data.

Here is a step-by-step guide to what the code does:

Import Pulumi Packages: Import the required Pulumi libraries for deployment.
Create a Feature Store: Define a Feature Store resource, where the name is the identifier, region is the location where the feature store will be created, and other properties that configure the feature store's behavior.
Export Outputs: After the deployment, Pulumi provides output values that you can use to reference or interact with the provisioned resources, such as the feature store's identifier.

import pulumi
import pulumi_gcp as gcp

# You must set the GCP region where you want to deploy your feature store
gcp_region = "<YOUR_GCP_REGION>"

# Create a centralized Feature Store for ML Models.
ai_feature_store = gcp.vertex.AiFeatureStore("central-feature-store",
                                             # Name of the feature store (must be unique within the GCP project)
                                             name="central-feature-store",
                                             # The GCP region where the feature store will be located
                                             region=gcp_region,
                                             # The GCP project ID - if not specified, it will use the default configured project
                                             project="<YOUR_GCP_PROJECT_ID>",
                                             # Optional specifications for encryption, serving configuration, etc.
                                             online_serving_config=gcp.vertex.AiFeatureStoreOnlineServingConfigArgs(
                                                 fixed_node_count=2
                                             ),
                                             # Description to provide more context about the feature store
                                             description="Centralized feature store for ML models"
                                             )

# Export the identifier of the feature store
pulumi.export("feature_store_id", ai_feature_store.id)

Replace <YOUR_GCP_REGION> and <YOUR_GCP_PROJECT_ID> with your actual GCP region and project ID.

To apply this Pulumi program:

Save the code in a file named __main__.py.
Make sure you have Pulumi installed and configured for use with your GCP account.
Run pulumi up in the command line in the same directory as your code.

This will start the deployment process of your feature store on GCP. Once completed, you can interact with the feature store using GCP's APIs or SDKs as part of your machine learning workflows.

For more detailed information about the Pulumi resource used, visit the pulumi_gcp.vertex.AiFeatureStore documentation.