1. Managed NoSQL Databases for Machine Learning Metadata Storage


    To store metadata for machine learning workloads in a managed NoSQL database, we can use a cloud provider's NoSQL database service. In this case, I’ll demonstrate how to provision such a service using Pulumi with Google Cloud Platform’s (GCP) Vertex AI Metadata Store.

    GCP's Vertex AI Metadata Store provides a managed service to store, retrieve, and manage metadata for AI models and their components. The service is fully managed, meaning that you don't need to provision any servers or worry about scaling or managing the infrastructure. It also integrates well with the rest of GCP's AI and machine learning services.

    Below is the Pulumi program in Python that shows how to create an AI Metadata Store using the gcp.vertex.AiMetadataStore resource. The program will define the metadata store with a given name and region, suitable for associating dataset and model information necessary in ML pipeline runs.

    import pulumi import pulumi_gcp as gcp # AI Metadata Store is a part of Google Cloud's Vertex AI services. It allows you to manage metadata for datasets, # machine learning models, and the components of your ML pipelines. Metadata is crucial for managing and monitoring # ML operations, including experiments, auditing, and reproducing ML models. A Vertex AI Metadata Store provides a # centralized repository for storing this type of metadata. # Here's how to create a metadata store using Pulumi. # Replace 'my-project' with your GCP project ID and 'us-central1' with the region you want to deploy the resources in. project_id = 'my-project' region = 'us-central1' # Creating a Vertex AI Metadata Store metadata_store = gcp.vertex.AiMetadataStore("metadata-store", project=project_id, region=region, description="A metadata store for machine learning datasets and models." ) # To access the created metadata store, you can export the store's ID. pulumi.export('metadata_store_id', metadata_store.id) # To connect with this metadata store from your ML applications and services, you would typically use the Vertex AI SDK or REST API. # The exported store ID would be used to reference it in your code or API requests.

    This program defines an AI Metadata Store in the us-central1 region, and it sets a description for clarity. If you have an existing GCP project, replace 'my-project' with your GCP project ID, and replace 'us-central1' with the region in which you want to create the metadata store.

    The metadata_store_id is exported so you can reference this particular metadata store when you are working with datasets and models within your ML applications. The interaction with the metadata store would typically be through GCP’s SDKs or REST APIs in the application code, which would utilize this store ID for operations such as logging and querying metadata.