1. Automated Metadata Tracking for Deep Learning Models with Databricks Metastore


    In the context of machine learning, metadata tracking is critical for understanding, reproducing, and managing the lifecycle of machine learning models. Metadata includes details like the version of the model, the data used to train it, its performance metrics, and versioning of its artifacts.

    Databricks offers a feature called the Databricks Metastore, which is a managed Hive Metastore service that provides robust scalability, reliability, and security for metadata management. It allows collaboration among data scientists, data engineers, and business analysts by providing a unified view of all their data.

    To automate metadata tracking for deep learning models within the Databricks ecosystem, you would typically use the Databricks Metastore. The Metastore integrates with MLflow, an open-source platform for managing the end-to-end machine learning lifecycle. This includes experiment tracking and packaging code into reproducible runs, and then recording and comparing results and models.

    Below is a Pulumi Python program that demonstrates how to set up resources for automated metadata tracking for deep learning models using Databricks Metastore. This program sets up a Metastore, adds a service principal for authentication, and sets up metastore data access with permissions.

    import pulumi import pulumi_databricks as databricks # Create a new Databricks Metastore metastore = databricks.Metastore("myMetastore", name="metastore-name", cloud="AWS", # The cloud provider where you're running Databricks owner="owner@yourdomain.com", # The owner of the Metastore region="us-west-2" # Region where the metastore is deployed ) # Create a service principal for accessing the Metastore metastore_provider = databricks.MetastoreProvider("myMetastoreProvider", name="metastore-provider-name", authentication_type="SERVICE_PRINCIPAL", recipient_profile_str="service-principal-secret" # Insert the actual service principal credential ) # Assign the newly created Metastore to a Databricks workspace metastore_assignment = databricks.MetastoreAssignment("myMetastoreAssignment", metastore_id=metastore.metastore_id, workspace_id=123456789 # Your Databricks workspace ID ) # Set up data access permissions for the Metastore metastore_data_access = databricks.MetastoreDataAccess("myDataAccess", name="data-access-name", owner="data-access-owner@yourdomain.com", metastore_id=metastore.metastore_id, # The following exemplary permissions are for AWS IAM roles aws_iam_role=databricks.MetastoreDataAccessAwsIamRoleArgs( role_arn="arn:aws:iam::123456789012:role/MetastoreRole" # ARN of the IAM role for accessing the Metastore data ) ) # Export the Metastore URL for client configuration pulumi.export('metastore_url', f"https://{metastore_assignment.metastore_id}.metastore.databricks.com")

    This code does the following:

    • It creates a new Databricks Metastore (databricks.Metastore) which is a managed Hive Metastore compatible with Databricks.
    • It sets up a service principal (databricks.MetastoreProvider) for authentication to interact with the Metastore which involves securely providing service principal credentials.
    • It assigns the Metastore to your specific Databricks workspace (databricks.MetastoreAssignment) which allows the workspace to interact with the Metastore.
    • It configures data access (databricks.MetastoreDataAccess) to define who has the permission to read/write to the Metastore, in this case, utilizing an AWS IAM role for demonstration purposes.
    • Finally, the Metastore URL is exported which is used for accessing the Metastore externally.

    With the Databricks Metastore in place and properly configured, you can now use it to store and track metadata about your deep learning models. You can include integration with MLflow to automatically capture and store this metadata as part of your training experiments.

    Please note that the details such as the recipient_profile_str for databricks.MetastoreProvider and other sensitive information should be securely managed and possibly provided via secrets management features like Pulumi's Config Secrets.