1. High-Availability Feature Stores for Machine Learning


    To create a high-availability feature store for machine learning, we need to address the underlying components that provide the necessary capabilities, such as storage, compute, and data processing. For the cloud provider, let's consider Azure as an example.

    Azure provides various services that can be leveraged to build a feature store, including Azure Machine Learning, Azure Databricks, and Azure Cosmos DB.

    Here's a Pulumi program in Python that demonstrates how you might set up the infrastructure for a high-availability feature store using Azure services:

    • Azure Machine Learning: An end-to-end platform for building, training, and deploying machine learning models. It includes a workspace that provides a centralized place to work with all the artifacts you create when you use Azure Machine Learning.
    • Azure Cosmos DB: A globally distributed, multi-model database service that scales throughput and storage across any number of geographic regions with a comprehensive SLA.

    I'll outline a program that sets up:

    1. An Azure Machine Learning Workspace to coordinate the machine learning workflow.
    2. An Azure Cosmos DB account to store features with high availability and global distribution capabilities.
    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import machinelearningservices from pulumi_azure_native import documentdb as cosmosdb # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('my-resource-group') # Create an Azure Machine Learning Workspace ml_workspace = machinelearningservices.Workspace("mlWorkspace", resource_group_name=resource_group.name, location=resource_group.location, identity=azure_native.machinelearningservices.IdentityArgs( type="SystemAssigned" ), sku=machinelearningservices.SkuArgs( name="Enterprise" ), storage_account=azure_native.machinelearningservices.StorageAccountIdentityArgs( resource_id= # Storage Account resource ID ), key_vault=azure_native.machinelearningservices.KeyVaultIdentityArgs( resource_id= # Key Vault resource ID ), app_insights=azure_native.machinelearningservices.AppInsightsIdentityArgs( resource_id= # Application Insights resource ID ), container_registry=azure_native.machinelearningservices.ContainerRegistryIdentityArgs( resource_id= # Container Registry resource ID ), opts=pulumi.ResourceOptions(depends_on=[resource_group]) ) # Create an Azure Cosmos DB account with the SQL API cosmos_db_account = cosmosdb.DatabaseAccount("cosmosDbAccount", resource_group_name=resource_group.name, location=resource_group.location, database_account_offer_type="Standard", locations=[cosmosdb.LocationArgs( location_name=resource_group.location, failover_priority=0, is_zone_redundant=False, )], consistency_policy=cosmosdb.ConsistencyPolicyArgs( default_consistency_level="Session", max_staleness_prefix=100, max_interval_in_seconds=5 ), enable_automatic_failover=True, enable_multiple_write_locations=True, is_virtual_network_filter_enabled=False, disable_key_based_metadata_write_access=False, opts=pulumi.ResourceOptions(depends_on=[resource_group]) ) # Export the Azure Machine Learning Workspace URL pulumi.export('ml_workspace_url', ml_workspace.discovery_url) # Export the Azure Cosmos DB account endpoint pulumi.export('cosmos_db_account_endpoint', cosmos_db_account.document_endpoint)

    In this program:

    • We create a new resource group for our machine learning infrastructure.
    • We establish an Azure Machine Learning workspace within that group, configured for enterprise-use.
    • We set up an Azure Cosmos DB account designed for session-level consistency with automatic failover, and it can accept writes in multiple locations.
    • We export the URLs for both the machine learning workspace and the Cosmos DB account so we can easily access them later.

    This approach ensures high availability by leveraging Azure's redundancy and failover features. The Cosmos DB's strength in providing globally distributed and scalable data storage solutions makes it a solid choice for a feature store's backend.

    Please note that you will need to replace placeholder comments (like # Storage Account resource ID) with appropriate resource identifiers or configuration objects; this sample assumes you have these resources such as a Storage Account and Key Vault already defined.