1. Deploying ML Model as a Service on Azure App Service


    To deploy a Machine Learning (ML) model as a service on Azure, we'll use Azure App Service, a fully managed platform for building, deploying, and scaling your web apps. Azure App Service is an ideal platform to host your ML model because it supports multiple languages and frameworks, easily integrates with other Azure services, and provides features like autoscaling, authentication/authorization, and custom domains.

    Our goal is to create an Azure App Service hosting environment where your ML model can run as a RESTful service. To do so, we will define an App Service Plan which specifies the underlying VM where your app will run, and the App Service which will actually host the application.

    We will also set up the necessary application settings and possibly other services that are needed to run the ML model, such as a storage account or a database if your application requires one.

    Here is a step-by-step Pulumi program written in Python to deploy an ML Model as a Service on Azure App Service:

    1. Set up an Azure Resource Group: All Azure resources need to be grouped into a resource group.
    2. Create an App Service Plan: This defines the performance and scaling characteristics of your app.
    3. Deploy the ML Model as a Web App: A web app in Azure App Service will host your ML model. We need to configure it with the necessary runtime and any application settings or connection strings.
    4. Configure Application Insights for monitoring: This step is optional but recommended to monitor the performance and detect issues with your ML service.

    Please note that this program assumes you have already packaged your ML model as a web application using a framework like Flask, Django, or FastAPI, which exposes the model as a REST API and that you have a Docker container image ready to be deployed to the Azure Container Registry (ACR) or Docker Hub.

    Let's write the Pulumi code to accomplish this:

    import pulumi import pulumi_azure_native as azure_native # Step 1: Set up an Azure Resource Group resource_group = azure_native.resources.ResourceGroup("ml_resource_group") # Step 2: Create an App Service Plan app_service_plan = azure_native.web.AppServicePlan("ml_app_service_plan", resource_group_name=resource_group.name, sku=azure_native.web.SkuDescriptionArgs( name="B1", # Basic tier, small VM tier="Basic", size="B1", family="B", capacity=1 ), kind="Linux", # Assuming Linux for a containerized app reserved=True # Indicates that the App Service Plan is Linux ) # Step 3: Deploy the ML Model as a Web App with a Docker image ml_web_app = azure_native.web.WebApp("ml_model_service", resource_group_name=resource_group.name, server_farm_id=app_service_plan.id, site_config=azure_native.web.SiteConfigArgs( linux_fx_version="DOCKER|<your-docker-image>", # Specify your Docker image app_settings=[ azure_native.web.NameValuePairArgs( name="WEBSITES_ENABLE_APP_SERVICE_STORAGE", value="false" ), # Add more settings as required ] ), https_only=True # Enforce HTTPS for added security ) # Optional Step 4: Configure Application Insights (omitted for brevity) # Export the URL of the ML Model Service pulumi.export("ml_model_service_url", ml_web_app.default_site_hostname)


    • We create an Azure resource group to contain and organize the resources we'll deploy.
    • An App Service Plan is created which defines the underlying resources such as the VM size and scaling capabilities. We've used the "Basic" tier here, but you can scale it up as needed.
    • With the WebApp resource, we deploy your Dockerized Python app, in this example "DOCKER|<your-docker-image>" should be replaced with your actual Docker image reference.
    • App settings are key-value pairs that your web app needs. We've set WEBSITES_ENABLE_APP_SERVICE_STORAGE to false indicating that the app's file system will be read-only.
    • We are enforcing HTTPS by setting https_only to True.
    • After deploying, the program exports the hostname of the service, which can be used to access your ML model's REST endpoint.

    Please make sure to replace <your-docker-image> with the actual image URL of your Dockerized machine learning model. This should be hosted in a container registry that Azure can access like Azure Container Registry or Docker Hub.

    Before running this program, make sure you have set up the Pulumi CLI and logged in to your Azure account through az login. Then, you can run pulumi up in the same directory as this program to deploy your infrastructure.