A/B Testing AI Models with Azure App Service Slots.

Question

Pulumi · Accepted Answer

A/B testing AI models with Azure App Service Slots involves setting up two versions of your application, each with its own AI model, and then directing user traffic between them to test which model performs better. Azure App Service provides a feature called "deployment slots", which are live apps with their own hostnames. You can use these slots to deploy different versions of your app.

When you are ready to A/B test your AI models, you can adjust the traffic percentages directing to each slot using the Azure portal or Azure CLI. In Pulumi, you can define your infrastructure as code, which includes App Service and the deployment slots, but the dynamic traffic routing needs to be adjusted outside of Pulumi, as it is an operational task.

Below, I will present a Pulumi program written in Python to create an Azure App Service with two slots for A/B testing AI models. Each slot will be set up as if it could host a different version of an AI model, but remember to deploy your actual application code with the models separately.

First, make sure you have the Azure provider for Pulumi installed and configured. You can find guidance on setting up the Azure provider in the [Pulumi Azure documentation](https://www.pulumi.com/docs/intro/cloud-providers/azure/setup/).

This program will perform the following actions:
- Create a resource group in Azure to organize all the resources.
- Create an App Service Plan, which defines the underlying hosting capabilities for the App Service.
- Create the main App Service, which will host the application and the AI models.
- Create two deployment slots, named "model-a" and "model-b", each represents a different AI model you want to A/B test.

Here's the full Pulumi program to set this up:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create a new resource group for the App Service and related resources
resource_group = azure_native.resources.ResourceGroup('a-b-testing-rg')

# Create an App Service Plan to define the hosting capabilities
app_service_plan = azure_native.web.AppServicePlan('a-b-test-plan',
    resource_group_name=resource_group.name,
    kind='App',    # 'App' for WebApps or 'FunctionApp' for Function Apps
    sku=azure_native.web.SkuDescriptionArgs(
        name='B1',  # Choose a pricing tier appropriate for your use case
        tier='Basic',
        size='B1',
        family='B',
        capacity=1,
    ),
    location=resource_group.location,
    reserved=False,  # Set to True if using a Linux plan
)

# Create the main App Service
app_service = azure_native.web.WebApp('main-app-service',
    resource_group_name=resource_group.name,
    location=resource_group.location,
    server_farm_id=app_service_plan.id,
)

# Create a deployment slot for Model A
model_a_slot = azure_native.web.WebAppSlot('model-a-slot',
    name=app_service.name,
    resource_group_name=resource_group.name,
    location=app_service.location,
    server_farm_id=app_service_plan.id,
    slot='model-a',
)

# Create a deployment slot for Model B
model_b_slot = azure_native.web.WebAppSlot('model-b-slot',
    name=app_service.name,
    resource_group_name=resource_group.name,
    location=app_service.location,
    server_farm_id=app_service_plan.id,
    slot='model-b',
)

# Export the URLs for the main app and each deployment slot
# You can use these URLs to configure traffic routing for A/B testing
pulumi.export('main_app_service_url', app_service.default_host_name)
pulumi.export('model_a_slot_url', model_a_slot.default_host_name)
pulumi.export('model_b_slot_url', model_b_slot.default_host_name)
```

This program initializes the required Azure resources for running A/B tests between AI models. You can export the URLs of the main App Service and the slots to view and manage them in the Azure portal.

Remember that this is only the infrastructure setup. You need to deploy your application with different AI models to the respective slots and then manage the traffic routing weights manually via the Azure portal or Azure CLI to effectively perform A/B testing.

Make sure to deploy this Pulumi program with `pulumi up`. You will be prompted to review and approve the deployment; once you do, Pulumi will provision the resources described in the code.