1. Integration of AI Workflows with Azure Functions and Event Hubs


    To achieve the integration of AI workflows with Azure Functions and Event Hubs, we will create an Azure Event Hub to handle the event messaging, and an Azure Function to process and execute AI tasks whenever a new event is received.

    Here’s what we’ll do step by step:

    1. Create an Azure Event Hub: This will act as the event ingestion point where events are sent, which could signal new data ready for processing or other relevant events for AI workflows.

    2. Create an Azure Function: This function will be triggered by the events posted to the Event Hub. The body of the function will contain the logic for the desired AI processing; for the sake of this integration example, we'll only set up the function and define a basic trigger.

    3. Integrate Azure Functions with the Event Hub: The function will be set up with an Event Hub trigger so that it executes the AI workflow whenever a new event is received.

    Let's put this into a Pulumi Python program:

    import pulumi import pulumi_azure as azure # Create an Azure Resource Group to hold our infrastructure resource_group = azure.core.ResourceGroup("ai_workflow_rg") # Create an Azure Event Hubs Namespace eventhub_namespace = azure.eventhub.EventHubNamespace("ai_workflow_event_hub_ns", resource_group_name=resource_group.name, sku="Standard", location=resource_group.location) # Create an Event Hub within the Namespace event_hub = azure.eventhub.EventHub("ai_workflow_event_hub", namespace_name=eventhub_namespace.name, resource_group_name=resource_group.name, partition_count=2, message_retention=1) # Create an Azure Function App function_app = azure.appservice.FunctionApp("ai_workflow_function_app", resource_group_name=resource_group.name, app_service_plan_id=app_service_plan.id, app_settings={ "FUNCTIONS_EXTENSION_VERSION": "~3", "FUNCTIONS_WORKER_RUNTIME": "python", # Event Hub connection string and other environment variables can be set here }) # Create an Event Hub triggered Function that integrates with our Event Hub eventhub_triggered_function = azure.eventhub.EventHubEventSubscription("ai_eventhub_triggered_function", function_app_name=function_app.name, event_hub_id=event_hub.id, consumer_group="$Default") # Export the URLs/endpoints that can be used to interact with our infrastructure pulumi.export('resource_group_name', resource_group.name) pulumi.export('event_hub_namespace_name', eventhub_namespace.name) pulumi.export('event_hub_name', event_hub.name) pulumi.export('function_app_name', function_app.name)


    • Resource Group: We start by creating an Azure Resource Group named ai_workflow_rg, which is a collection under which all our Azure resources will be created. This helps in managing them collectively.

    • Azure Event Hubs Namespace: This namespace, ai_workflow_event_hub_ns, provides a unique scoping container in which we house our Event Hub. We choose the "Standard" SKU because this example is basic; a more complex application might require a higher-performance SKU.

    • Azure Event Hub: The ai_workflow_event_hub is created within the namespace. This Event Hub will be the ingestion point for events related to our AI workflow. We configure it with a partition count and message retention suitable for a simple example.

    • Azure Function App: The ai_workflow_function_app holds the functions and executes the code in response to triggers. Here, we prepare an Azure Functions App to run Python code responding to Event Hub triggers. The environment variables required for the function, including the Event Hub connection string, would be provided via app_settings.

    • Event Hub Triggered Function: ai_eventhub_triggered_function represents the integration point between the Azure Function and the Event Hub, linking the two resources so that when an event is sent to the Event Hub, it triggers the Azure function to run the AI workflow's logic.

    • Exports: Finally, we’re exporting the names of the resource group, Event Hub namespace, Event Hub, and Function App. These can be used to identify or access resources within the Azure portal or via the Azure CLI.

    Next Steps:

    To actually implement an AI workflow:

    1. You would need to code the logic in the function. This involves creating an Azure Function with Python that uses machine learning libraries or calls out to Azure Cognitive Services.
    2. You could enhance the Event Hub to handle specific patterns of event streams or to work with multiple consumer groups.
    3. You must secure your Azure Function app by configuring the appropriate authentication and authorization settings.

    This setup provides a foundation on which you can develop complex AI integrations using Pulumi to automate the provisioning and management of your Azure cloud infrastructure.