1. Centralized Metadata Store for Machine Learning Datasets


    To create a centralized metadata store for machine learning datasets, we can use one of the cloud providers that offer machine learning services with integrated metadata management. In this example, we will use Google Cloud's Vertex AI Metadata Store. This is a service that provides a way to store and manage metadata for datasets used in machine learning workflows, helping to organize, track, and audit metadata across machine learning pipelines.

    Key Concepts:

    • Vertex AI Metadata Store: A component of Vertex AI that helps you to manage the metadata for machine learning workflows. Metadata is information about other data, which helps give it context. For machine learning, this can include details on datasets, models, metrics, and more.
    • Dataset: Represents a collection of data that can be used for training machine learning models. In the context of the metadata store, this typically includes metadata about the structure, contents, and source of the dataset.
    • Metadata Schema: Defines the structure of metadata. In Vertex AI, you can either use predefined schemas or define your own custom schemas to describe the metadata associated with your datasets, models, and other AI workloads.

    Below is a Pulumi program in Python that sets up an AI Metadata Store in Google Cloud. The program also demonstrates how to create a dataset with basic metadata. This setup assumes you've already configured your Google Cloud credentials for use with Pulumi.

    import pulumi import pulumi_gcp as gcp # Create an AI Metadata Store metadata_store = gcp.vertex.AiMetadataStore("central-metadata-store", project="<Your Google Cloud Project ID>", location="us-central1", # Choose the appropriate location description="Central Metadata Store for ML Datasets" ) # Metadata schema for the AI Dataset # You can define your own custom schema or use a predefined one. # Visit https://cloud.google.com/vertex-ai/docs/datasets/prepare-metadata-schema for # more information on preparing a metadata schema. metadata_schema_uri = "gs://google-cloud-aiplatform/schema/dataset/metadata/image_1.0.0.yaml" # Create an AI Dataset with metadata ai_dataset = gcp.vertex.AiDataset("ml-dataset", project="<Your Google Cloud Project ID>", location="us-central1", # Must match the location of the metadata store display_name="ML Training Dataset", metadata_schema_uri=metadata_schema_uri, labels={ "env": "production", "team": "data-science" } ) # Export the metadata store and dataset ID for reference pulumi.export('metadata_store_id', metadata_store.name) pulumi.export('dataset_id', ai_dataset.name)

    What the Program Does:

    1. Creation of Metadata Store: The gcp.vertex.AiMetadataStore resource creates a new Vertex AI Metadata Store in the specified location and project. This metadata store will be the central place to store and manage metadata for your ML datasets.

    2. Setting up a Dataset: The gcp.vertex.AiDataset resource sets up a dataset in the metadata store. It includes essential metadata like a schema URI, display name, and any relevant labels to categorize and describe the dataset.

    3. Exporting Identifiers: After creating the metadata store and dataset, their identifiers are exported. These can be used to reference the resources in other parts of your Pulumi stack or infrastructure.

    Remember to replace <Your Google Cloud Project ID> with your actual Google Cloud project ID.

    This program will set up the infrastructure for managing metadata about machine learning datasets. As next steps, you would populate the dataset with actual metadata, version the metadata, and use it to track datasets associated with machine learning experiments and training jobs.