Scheduled AI Model Retraining with Azure Logic Apps

Question

Pulumi · Accepted Answer

To set up scheduled AI model retraining using Azure Logic Apps, you'll need to create an Azure Logic App that gets triggered on a schedule you define. The Logic App will then run a workflow that could be designed to trigger an Azure Machine Learning pipeline responsible for the retraining task or it could directly invoke model training scripts, depending on your AI workflow.

To give you a detailed understanding, let's break it down into steps using Pulumi with Python:

1. **Set up an Azure Logic App**: This is the core service that will orchestrate the retraining process on a schedule that you specify.

2. **Define a Workflow for the Logic App**: The workflow will contain the actions that need to be performed for retraining the model. This could include fetching new data, running training scripts, evaluating the model performance, and possibly updating the model version in the Azure Machine Learning registry.

3. **Define a Schedule for the Logic App**: Scheduling can be done using a trigger in the Workflow definition that specifies when the workflow should be executed (e.g., every day at midnight).

4. **Integrate Azure Machine Learning**: If you're using Azure Machine Learning, you can have the Logic App trigger a Machine Learning pipeline that handles the retraining.

Below is the Pulumi program that accomplishes the above steps:

```python
import pulumi
import pulumi_azure_native as azure_native

# Define the resource group to hold all related resources
resource_group = azure_native.resources.ResourceGroup("resource_group")

# Create a Logic App Workflow that will contain our scheduled process
logic_app_workflow = azure_native.logic.Workflow("workflow",
    location=resource_group.location,
    resource_group_name=resource_group.name,
    definition={
        # Define triggers, schedule, and actions according to your needs
        "triggers": {
            "Recurrence": {
                "type": "Recurrence",
                "recurrence": {
                    "frequency": "Day",  # This can be "Minute", "Hour", "Week", etc.
                    "interval": 1,  # This defines how often the trigger fires
                    # Optionally, more fine-grained schedule settings could be provided here
                }
            }
        },
        "actions": {
            "AzureML_Retrain": {
                "type": "Http",  # Depending on your setup, this could be a direct Http call to Azure ML or another mechanism
                "inputs": {
                    "method": "POST",
                    "uri": "Your Azure ML training endpoint or pipeline"
                    # Additional necessary headers or authentication details go here
                }
            }
        }
    },
    parameters={
        "$connections": {
            "value": {
                # Connections to other Azure services if necessary
            }
        }
    }
)

# Export the Logic App Workflow URL which you can use for checking the workflow status or triggering it manually if required
pulumi.export('logic_app_workflow_url', logic_app_workflow.endpoint_url)
```

In this program:

- We first create a Resource Group to hold our Pulumi-managed Azure resources.
- A Logic App Workflow (`azure_native.logic.Workflow`) is defined to orchestrate the retraining tasks which include triggers and actions.
  - In the "triggers" section, we define a recurrence trigger that sets the schedule for the model retraining.
  - The "actions" section is where the actual retraining steps are outlined. In this template, we use a 'Http' action type placeholder where you would make a call to your Azure Machine Learning training endpoint. You need to replace the "uri" and potentially add more details such as headers and authentication specifics.
  
Keep in mind, the provided code is a high-level template and you will need to customize the triggers, actions, and connections to match your specific use case and workflow. You would need the specific URI for your Azure Machine Learning pipeline or endpoint that handles retraining.

Furthermore, you may need to configure additional services such as Azure Storage for data handling or integrate with Azure Machine Learning service for managing the ML lifecycle, depending on the complexity of your AI model retraining process.

Once you run the Pulumi program, it will set up the infrastructure as per the above specifications. You will be able to see the URL of the Logic App Workflow exported, which you can use to interact with the workflow as necessary.