1. Event-driven ML Workflows with Azure Event Grid


    In order to create an event-driven Machine Learning (ML) workflow using Azure Event Grid, you will need to define a few key components in your infrastructure. These typically include:

    1. An Event Source: Where your events are generated. This could be anything from blob storage where you store your model data, a web app that captures user behavior, or IoT devices streaming data.

    2. Azure Event Grid: A service that routes events from your event sources to subscribers. It provides reliable event delivery, advanced routing, and filtering capabilities.

    3. Event Subscribers: The recipients of the events. This could be an Azure Function that triggers an ML pipeline, a Logic App that orchestrates complex workflows, or even another Azure service like Azure Automation.

    4. An ML environment: This could be Azure Machine Learning Service, where you would deploy, manage, and monitor your ML models.

    Let's write a Pulumi program in Python that sets up a simple event-driven ML workflow using Azure Event Grid. In this illustration, we'll set up an Event Grid domain, an Event Grid topic within the domain, and a subscription for the topic. The subscription will send events to a placeholder Azure Function, which you can assume will trigger some ML process upon receiving an event.

    import pulumi import pulumi_azure_native as azure_native # Replace these values with your own unique names and actual function app settings if necessary resource_group_name = 'my-resource-group' event_grid_domain_name = 'my-event-grid-domain' event_grid_topic_name = 'my-event-grid-topic' function_app_name = 'my-azure-function' # Create an Azure Resource Group to organize your Azure resources resource_group = azure_native.resources.ResourceGroup('resource-group', resource_group_name=resource_group_name) # Create an Azure Event Grid Domain event_grid_domain = azure_native.eventgrid.Domain('event-domain', resource_group_name=resource_group.name, domain_name=event_grid_domain_name, location=resource_group.location) # Create an Azure Event Grid Topic within the domain event_grid_topic = azure_native.eventgrid.Topic('event-topic', resource_group_name=resource_group.name, domain_name=event_grid_domain.name, topic_name=event_grid_topic_name) # In a real scenario, you would get the Function App endpoint and its key. # For now, we'll use a placeholder URL and key for the Azure Function that should handle the events. function_app_url = "https://my-function-app.azurewebsites.net/api/myeventhandler" function_app_key = "x-functions-key-placeholder" # Create an Event Grid Subscription for the Event Grid Topic event_subscription = azure_native.eventgrid.EventSubscription('event-subscription', scope=event_grid_topic.id, destination=azure_native.eventgrid.WebHookEventSubscriptionDestinationArgs( endpoint_url=function_app_url, endpoint_baseline_url=function_app_url, max_events_per_batch=1, preferred_batch_size_in_kilobytes=64, ), filter=azure_native.eventgrid.EventSubscriptionFilterArgs( included_event_types=["Microsoft.MachineLearningServices.ModelRegistered"], # Listening to a specific ML-related event ), event_delivery_schema='EventGridSchema', retry_policy=azure_native.eventgrid.RetryPolicyArgs( max_delivery_attempts=3, event_time_to_live_in_minutes=1440, ), ) # Export the Event Grid Topic Endpoint URL pulumi.export('event_grid_topic_endpoint', event_grid_topic.endpoint) # Export the Event Subscription ID pulumi.export('event_subscription_id', event_subscription.id)

    This Pulumi program creates the infrastructure required to set up an event-driven ML workflow that is reactive to specific events, in this case, the registration of a new machine learning model.

    Here's what each section does:

    • Resource Group: A logical container for your Azure resources. Good practice for organization and managing access.

    • Event Grid Domain: Acts as the root for all the event topics you create.

    • Event Grid Topic: Represents a specific category or type of events that you want to route.

    • Event Grid Subscription: Subscribes to the Event Grid Topic and routes events to the Azure Function. It filters to only proceed with specific event types (like ones signifying a new ML model is registered) and defines a webhook as the endpoint to handle the events.

    • Exported Values: Outputs of the topic endpoint and subscription ID for integration and debugging purposes.

    Remember to replace the placeholders with actual values from your Azure Function app (URL and key) to handle the event-driven ML workflow. The example shows a webhook destination for simplicity, but you could also route the events to other Azure services depending on your workflow's needs.

    With this foundation, you can build more complex ML workflows by adding additional event sources and subscribers, defining event handlers that process data, train models, and perform predictions according to your business logic.