1. AI Model Configuration Store with Rollback Capabilities


    To create an AI model configuration store with rollback capabilities, you would want to manage your model configurations as versioned artifacts in a system that supports versioning and access controls. You can use cloud services such as Google Cloud's Vertex AI or Azure Machine Learning for handling AI models and feature stores, and the Azure App Configuration Store for managing configurations with versioning and rollback support.

    In Google Cloud, Vertex AI Feature Store can be used to store, serve, and share machine learning features. For versioning of AI models and configurations, Vertex AI Metadata Store can be used. Similarly, Azure provides Machine Learning Services, that supports versioning of machine learning models and datasets, and Azure App Configuration to manage and store configurations with feature flags, which can provide the capability to rollback to previous configurations if needed.

    The following Pulumi Python code sets up an AI feature store and a metadata store in Google Cloud, and an App Configuration Store in Azure. These services will be leveraged to create a system where you can store your AI model configurations and roll back to earlier versions if necessary.

    import pulumi import pulumi_azure_native as azure_native import pulumi_gcp as gcp # Set up a Google Cloud AI Feature Store to store ML features. ai_feature_store = gcp.vertex.AiFeatureStore( "my-feature-store", project="your-gcp-project-id", region="us-central1", name="feature-store" ) # The Metadata Store in Vertex AI to manage versions and metadata of the models. ai_metadata_store = gcp.vertex.AiMetadataStore( "my-metadata-store", project="your-gcp-project-id", region="us-central1", name="metadata-store" ) # Configuration Store in Azure to handle configurations with rollback capabilities. configuration_store = azure_native.appconfiguration.ConfigurationStore( "my-configuration-store", resource_group_name="your-resource-group-name", location="eastus", sku=azure_native.appconfiguration.SkuArgs( name="Standard" ), config_store_name="config-store" ) # Expose the resource names for reference pulumi.export("feature_store_name", ai_feature_store.name) pulumi.export("metadata_store_name", ai_metadata_store.name) pulumi.export("configuration_store_name", configuration_store.name)

    To run this program:

    1. Ensure you have Pulumi installed and configured with access to Google Cloud and Azure.
    2. Save the above code in a file named main.py.
    3. Run pulumi up from the command line in the same directory as your main.py file to deploy the resources.

    Explanation of resources used:

    • gcp.vertex.AiFeatureStore: This resource sets up a Feature Store in Google Cloud that helps in storing, serving, and sharing ML features effectively.

    • gcp.vertex.AiMetadataStore: This is used to create a Metadata Store within Vertex AI that manages metadata and versions of AI models, enabling tracking and version comparisons of models.

    • azure_native.appconfiguration.ConfigurationStore: This creates a configuration store resource in Azure, where you can store configurations for your application and use feature flags to implement conditional access or gradual rollout of features. It supports versioning and rollback, which can be employed for managing the configuration of AI models.

    In practice, you need to interact with these services via their respective SDKs or APIs to store and manage your model configurations and roll back when needed. The above deployment is the infrastructure setup portion; the logic for versioning, storing configurations, and rolling back would be part of your application code that uses these services.