Traffic Routing for AI Services with Azure App Services.

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Pulumi · Accepted Answer

### Understanding Traffic Routing for AI Services with Azure App Services

Azure App Services is a fully managed platform for building, deploying, and scaling web apps. In the context of AI services, you might be hosting a machine learning model or any AI-driven application that requires scalable and reliable web hosting. Traffic routing in this setting is crucial for tasks like A/B testing, gradual rollouts, and high availability.

Here's a brief overview of how we will set up traffic routing for AI Services using Azure App Services:

1. **Azure App Service Plan**: This is essentially a hosting plan for your app that defines the region of the datacenter, size of the VM instances, and various other settings like scaling and deployment slots.

2. **Azure App Service**: This is the service that will host your AI application. You can define various settings such as auto-scaling, authentication mechanisms, connection strings, and more.

3. **Traffic Routing**: To control the distribution of traffic between different instances of your app or between different deployment slots, we can set up routing rules. Azure offers ways to adjust these rules based on various parameters such as weights, performance, and session information.

4. **Other Networking Details**: Azure allows setting up features like IP restrictions, enabling HTTPS, and setting up custom domain names, which can all be part of your traffic routing strategy.

In the following Pulumi program, we will set up an Azure App Service and an App Service Plan. We will also include the scaffolding where you would define traffic routing rules, although these specifics would need to be adjusted based on your particular needs and implementation of the AI services.

### Azure App Service and Traffic Routing with Pulumi

```python
import pulumi
import pulumi_azure_native as azure_native

# Define a Resource Group where all our resources will live
resource_group = azure_native.resources.ResourceGroup("ai_services_resource_group")

# Define an App Service Plan
app_service_plan = azure_native.web.AppServicePlan("ai_service_plan",
    resource_group_name=resource_group.name,
    kind="Linux", # Assuming a Linux-based app; change to "Windows" if required
    reserved=True, # Required for Linux plan
    sku=azure_native.web.SkuDescriptionArgs(
        tier="Standard",
        name="S1"
    )
)

# Define the App Service
app_service = azure_native.web.WebApp("ai_service_app",
    resource_group_name=resource_group.name,
    server_farm_id=app_service_plan.id,
    site_config=azure_native.web.SiteConfigArgs(
        app_settings=[
            azure_native.web.NameValuePairArgs(name="WEBSITE_RUN_FROM_PACKAGE", value="1") # Example setting
        ],
        # Add other configurations like connection strings, default documents, etc.
    ),
    https_only=True # Enforce HTTPS for the service
)

# Output the default host name for the App Service
pulumi.export('default_host_name', app_service.default_host_name)

# After deploying this Pulumi program, you can use the Azure Portal or Azure CLI to configure traffic routing settings such as:
# - Deployment slot creation and settings
# - Setting up testing in production (TiP) with route percentages
# - Adjusting custom domain names and SSL bindings
```

In the provided Pulumi program, we're deploying a new Azure App Service Plan and an Azure App Service. We're setting the `https_only` parameter to ensure all traffic uses HTTPS.

Once the initial setup is completed, you'll have an App Service up and running. The next step for setting up traffic routing, such as setting up Azure's Testing in Production (TiP) feature, will involve additional configuration that is typically performed in the Azure Portal or via the Azure CLI.

The `pulumi.export` at the end outputs the default hostname for the App Service, which gives you the URL at which your service will be available. You may use this URL to access your AI services once deployed.

Make sure to customize the App Service settings (`site_config`) and scale up options (`sku` in the `AppServicePlan`) to fit the requirements and workload of your AI services.