Dynamic AI Model Retraining Triggers with Event Grid

Question

Pulumi · Accepted Answer

To set up a dynamic AI model retraining trigger, we'll use Azure Event Grid, which enables you to easily build event-based architecture. Typically, you can listen to relevant events, such as the arrival of new data in a storage account which may indicate that it's time to retrain your AI model. You can then create an Event Grid subscription on that storage account to trigger a process, like an Azure Function, for retraining your model.

Here is how to get started with Pulumi to create an Event Grid subscription that watches for blob storage events.

First, you'll need an Azure Blob Storage Account and an Event Grid Topic. The storage account acts as the event source, while the Event Grid Topic is where events are sent. Then you'll create an Event Grid Subscription to that topic, which will forward the events to an Azure Function that performs the retraining.

The Azure Function isn't detailed in this template, but you would have that set up to listen to the topic and act accordingly.

Now, let's create this setup with Pulumi. The code includes comments that explain each step. Make sure you have the Pulumi CLI and Azure provider configured before running this code.

```python
import pulumi
import pulumi_azure_native as azure_native

# Resource Group
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Storage Account where the input data for retraining is stored
storage_account = azure_native.storage.StorageAccount("storageaccount",
    resource_group_name=resource_group.name,
    kind="StorageV2",
    sku=azure_native.storage.SkuArgs(name="Standard_LRS")
)

# Event Grid Topic where you will send your events
event_grid_topic = azure_native.eventgrid.Topic("eventgridtopic",
    resource_group_name=resource_group.name,
    location=resource_group.location
)

# Event Grid Subscription to the Storage Account
# Filters can be applied to listen to specific events (like blob created)
# Here we use default settings to listen to all blob events.
event_subscription = azure_native.eventgrid.EventSubscription("eventsubscription",
    resource_group_name=resource_group.name,
    scope=storage_account.id,
    event_delivery_schema="EventGridSchema",
    # Event handler destination (Azure Function, Logic App, etc.)
    # Here you replace with the details of your function or endpoint.
    destination=azure_native.eventgrid.EventHubEventSubscriptionDestinationArgs(
        resource_id="/subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.EventHub/namespaces/{namespace}/eventhubs/{eventhub}"  # Replace with actual resource ID
    )
)

# Export the primary endpoint to check the integration
pulumi.export("primary_endpoint", event_grid_topic.primary_endpoint)
```

In the `EventSubscription` resource, you would modify the `destination` field to point to your specific Azure Function or endpoint that will handle the retraining process. In place of the Event Hub details, you would provide the Azure Function details.

To execute this code, you should have Python installed with the Pulumi SDK and have configured your Azure credentials. Running `pulumi up` in your terminal, within the directory holding this script, will create the resources as defined.

When the resources have been created, you can upload blob storage items and watch as events are sent to the Event Grid Topic. Then, your subscription will trigger the attached function (or other endpoint), which can start the model retraining process.

Remember that you need to set the actual values for placeholders, such as `{subscription-id}`, `{resource-group}`, `{namespace}`, and `{eventhub}` with your actual Azure subscription and resource information.