1. ML Model Feature Store on BigQuery Analytics Hub


    To create an ML Model Feature Store on BigQuery Analytics Hub using Pulumi and Google Cloud Platform (GCP), we will leverage various GCP services, including BigQuery, and the BigQuery Analytics Hub to organize and make ML features available across your organization.

    BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. In the context of machine learning, it can be used to store and manage features used in training machine learning models. This is typically done through creating datasets and tables within BigQuery to store these features, and optionally, creating a feature store to organize and serve this information.

    The BigQuery Analytics Hub allows you to create and share datasets with other organizations securely, which is particularly powerful when working with machine learning features that you might want to share with different teams.

    Below is a Pulumi program written in Python which defines a feature store constructed through a BigQuery dataset. We use the bigquery.Dataset resource to define our feature store, and the bigquery.Table to represent a feature table within our dataset (feature store). We also define IAM members for the data exchange to manage access permissions.

    Pulumi Program - ML Model Feature Store

    import pulumi import pulumi_gcp as gcp # Set your project and location - replace with your project and location project = 'my-gcp-project' location = 'us-central1' # Create a BigQuery dataset to store features for ML models feature_store_dataset = gcp.bigquery.Dataset("feature_store_dataset", dataset_id="ml_feature_store", description="Dataset to store ML features", location=location, ) # Define a feature table within the dataset feature_table = gcp.bigquery.Table("feature_table", dataset_id=feature_store_dataset.dataset_id, table_id="ml_features", schema="""[ { "name": "feature_id", "type": "STRING", "mode": "REQUIRED" }, { "name": "feature_value", "type": "FLOAT64", "mode": "REQUIRED" }, { "name": "timestamp", "type": "TIMESTAMP", "mode": "REQUIRED" } ]""", expiration_time="2524604400000" # Set an expiration for the table e.g., 2050/01/01 in milliseconds ) # Create a Data Exchange on BigQuery Analytics Hub data_exchange = gcp.bigqueryanalyticshub.DataExchange("data_exchange", data_exchange_id="ml_model_feature_store", location=location, description="DataExchange for ML Model Feature Store", display_name="ML Model Feature Exchange", documentation="https://www.example.com/ml-model-feature-documentation", project=project ) # Specify IAM binding to control access to the Data Exchange data_exchange_iam_binding = gcp.bigqueryanalyticshub.DataExchangeIamBinding("data_exchange_iam_binding", data_exchange_id=data_exchange.data_exchange_id, role="roles/bigquery.dataEditor", members=[f"serviceAccount:feature_store@{project}.iam.gserviceaccount.com"], project=project, location=location, ) # Export URLs of created resources pulumi.export("feature_store_dataset_url", feature_store_dataset.self_link) pulumi.export("feature_table_url", feature_table.self_link) pulumi.export("data_exchange_url", data_exchange.self_link)

    In this program:

    • We create a Dataset with the ID ml_feature_store within BigQuery to act as our central repository for ML features.
    • We then define a Table called ml_features within this dataset which will hold the actual feature data. The schema for this table includes fields for a feature ID, feature value, and a timestamp.
    • To facilitate the sharing of these features, we create a DataExchange within BigQuery Analytics Hub. This creates a shareable asset that can be accessed by specific principals. The IAM binding is used to allow certain roles or members to access this data exchange.
    • Finally, we export the URLs of the created resources for easy access to their respective GCP Console pages.

    Remember to replace my-gcp-project, us-central1, and the service account email with the appropriate values for your environment. The service account should be created prior to running this pulumi program and given appropriate permissions to manage BigQuery resources.