Integrating Machine Learning Pipelines and Azure Services via Azure Event Grid

Question

Pulumi · Accepted Answer

Integrating machine learning pipelines with Azure services through Azure Event Grid involves creating a reactive, event-driven architecture. Azure Event Grid allows you to build applications with event-based architectures by subscribing to events that are triggered by Azure services or your own applications.

To integrate machine learning pipelines with Azure services via Event Grid, you would typically:

Publish custom events or listen to system events from Azure services that are relevant to the machine learning pipeline.
Create an Event Grid topic to which sources can send events.
Create an Event Grid subscription that reacts to the events by invoking an Azure Function, Logic App, or a webhook that integrates with your machine learning pipeline.

Let's consider a scenario where we want to integrate with Azure Machine Learning services. Whenever a new data blob is uploaded to an Azure Blob Storage container involved in our machine learning pipeline, it triggers an Event Grid event. The Event Grid then routes this event to an Azure Function that preprocesses the data and feeds it into an Azure Machine Learning model for scoring.

Below is a Pulumi program written in Python that sets up such an integration by:

Creating an Event Grid Topic to receive events.
Creating a Blob Storage account and container where the data for the ML model is stored.
Creating an Event Grid Event Subscription that triggers an Azure Function when new data is added to the Blob Storage container.

Please make sure you've Azure CLI installed and configured, as Pulumi will use it to access your Azure subscription.

Here's what the Pulumi program looks like:

import pulumi
import pulumi_azure_native as azure_native
from pulumi_azure_native import storage, eventgrid, resources

# Create an Azure Resource Group
resource_group = resources.ResourceGroup("resource_group")

# Create an Azure Storage Account
storage_account = storage.StorageAccount("storageaccount",
    resource_group_name=resource_group.name,
    sku=storage.SkuArgs(name=storage.SkuName.STANDARD_LRS),
    kind=storage.Kind.STORAGE_V2)

# Create a Storage Container
container = storage.BlobContainer("container",
    resource_group_name=resource_group.name,
    account_name=storage_account.name)

# Create an Azure Event Grid Topic
topic = eventgrid.Topic("topic",
    resource_group_name=resource_group.name,
    location=resource_group.location)

# Create an Azure Function (Assuming one already exists that we can link to)
# In a real-world scenario, you would also create the Function App here.
# For this example, we are using a placeholder URL for an existing Azure Function.
function_app_id = "<your-azure-function-app-id>"

# Create an Event Grid Subscription to the Blob Storage Container
subscription = eventgrid.EventSubscription("subscription",
    resource_group_name=resource_group.name,
    scope=f"/subscriptions/{pulumi.config.require('azure:subscriptionId')}/resourceGroups/{resource_group.name}/providers/Microsoft.Storage/storageAccounts/{storage_account.name}/blobServices/default/containers/{container.name}",
    destination=eventgrid.AzureFunctionEventSubscriptionDestinationArgs(
        resource_id=function_app_id,
    ),
    event_delivery_schema=eventgrid.EventDeliverySchema.EVENT_GRID_SCHEMA,
    filter=eventgrid.EventSubscriptionFilterArgs(
        included_event_types=["Microsoft.Storage.BlobCreated"],
    ))

pulumi.export("topic_name", topic.name)
pulumi.export("container_name", container.name)
pulumi.export("subscription_id", subscription.id)

This program performs the following actions:

It first creates a new Azure Resource Group to contain our resources.
Then it sets up a new Azure Storage Account and a Blob Container within that account.
It also creates an Azure Event Grid Topic within the same resource group.
Then it defines an Azure Function which presumably has the logic to process the data—the function_app_id should be replaced with the actual Function App resource ID.
Lastly, it sets up an Event Subscription on the Blob Container that we created, with a filter for the 'BlobCreated' event, so that any time a new blob is added, an event is sent to our Azure Function.

Make sure to replace <your-azure-function-app-id> with the actual ID of your Azure Function App where you have the logic for processing your ML pipeline.

Run pulumi up in your terminal to create and deploy these resources to your Azure subscription. Ensure you've set up Azure CLI authentication and have access to the subscription.

This program requires pre-existing knowledge of your Azure Function app, so you can integrate it properly. If this function doesn't exist yet, you would need another Pulumi program to define it, which could be created in a similar declarative fashion as the resources above.