1. Event-Driven Data Enrichment for Machine Learning


    To create an event-driven data enrichment process for Machine Learning (ML) applications, you would typically construct a pipeline that accomplishes the following:

    1. Ingests data from one or more sources.
    2. Processes and enriches the data, either in real-time or batch mode.
    3. Routes the enriched data to a machine learning service or stores it for training and batch prediction.

    To achieve this using cloud services, one may leverage a variety of components such as message queues, serverless functions, and ML services of cloud providers like Azure.

    Below is a Python program written with Pulumi that outlines an event-driven data enrichment setup using Azure services. This program includes:

    • An Azure Event Hub to ingest events in real-time.
    • An Azure Function triggered by Event Hub events to process and enrich data.
    • Azure Machine Learning Services to manage and deploy ML models.

    In this example, we assume that the Function app's code is already available, and we will only focus on creating the infrastructure. Comments in the code explain what each section does.

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import resources, eventhub, storage, web # Configuration and Resource Group resource_group = resources.ResourceGroup("my-ml-resource-group") # Event Hubs Namespace and Event Hub for ingesting events namespace = eventhub.EventHubNamespace("my-eventhub-namespace", resource_group_name=resource_group.name, location=resource_group.location, sku=eventhub.SkuArgs( name="Standard", tier="Standard" )) event_hub = eventhub.EventHub("my-eventhub", resource_group_name=resource_group.name, namespace_name=namespace.name) # Storage Account and Container for Function App storage_account = storage.StorageAccount("mystorageaccount", resource_group_name=resource_group.name, location=resource_group.location, sku=storage.SkuArgs(name="Standard_LRS"), kind=storage.Kind.STORAGE_V2) container = storage.BlobContainer("mycontainer", account_name=storage_account.name, resource_group_name=resource_group.name) # Function App triggered by Event Hub to process and enrich data app_service_plan = web.AppServicePlan("my-asp", resource_group_name=resource_group.name, location=resource_group.location, sku=web.SkuDescriptionArgs( name="Y1", tier="Dynamic" ), kind="FunctionApp") function_app = web.WebApp("my-function-app", resource_group_name=resource_group.name, server_farm_id=app_service_plan.id, site_config=web.SiteConfigArgs( app_settings=[ web.NameValuePairArgs(name="AzureWebJobsStorage", value=storage_account.primary_connection_string), web.NameValuePairArgs(name="FUNCTIONS_EXTENSION_VERSION", value="~3"), web.NameValuePairArgs(name="FUNCTIONS_WORKER_RUNTIME", value="python"), web.NameValuePairArgs(name="EventHubConnection", value=event_hub.id.apply(lambda id: f"Endpoint=sb://{namespace.name}.servicebus.windows.net/;SharedAccessKeyName=RootManageSharedAccessKey;SharedAccessKey={namespace.default_primary_key}")) ] ), kind="functionapp", location=resource_group.location) # Azure Machine Learning Workspace ml_workspace = azure_native.machinelearningservices.Workspace("my-ml-workspace", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs( name="Basic", tier="Basic" )) # Output the primary key of the Event Hub Namespace which will be used in the Function App primary_event_hub_key = pulumi.Output.all(resource_group.name, namespace.name)\ .apply(lambda args: azure_native.eventhub.list_namespace_keys(resource_group_name=args[0], namespace_name=args[1], authorization_rule_name="RootManageSharedAccessKey")\ .apply(lambda keys: keys.primary_key)) # Export the EventHub connection string to be used in Function App settings pulumi.export("Event Hub Connection String", primary_event_hub_key) # Note: The actual enrichment process handled within the Azure Function is not detailed here. # It would require writing the function code to process incoming events and manage interactions with Machine Learning services.

    This Pulumi program sets up the cloud resources needed to collect, process, and prepare data for machine learning workloads. Here's the breakdown:

    • EventHubNamespace and EventHub: These resources create a scalable event ingestion service that will receive incoming data streams.
    • StorageAccount and BlobContainer: Needed by the Azure Function for state management and logging.
    • AppServicePlan and WebApp: Deploys an Azure Function App in a Dynamic Plan (serverless) that will be triggered by events pushed to the Event Hub. The Function App is configured with required environment variables.
    • Workspace: Sets up an Azure Machine Learning workspace, which provides a central location for ML activities including model training and deployment.
    • pulumi.export: Exports the Event Hub connection string, which could then be used to configure other applications that need to integrate with the Event Hub.

    This program provides the infrastructure for data enrichment. Actual data processing logic must be implemented within the Azure Function code in Python or another language supported by Azure Functions. Azure Machine Learning Workspace is prepared to manage ML models, but specific model training and deployment tasks are not covered by this Pulumi program.