1. Multi-tenant AI Model Serving with Azure App Services


    To serve an AI model in a multi-tenant architecture using Azure App Services, you need to consider the setup of Azure resources that would allow you to host and run your model, manage incoming requests, and potentially scale to support multiple tenants. For this, you can leverage the azure-native.web.AppServicePlan and azure-native.web.WebApp from the Pulumi Azure Native provider to create an App Service Plan and a Web App respectively. Here is what you need to do:

    1. App Service Plan: This resource acts as a hosting plan defining how many resources your App Service will have, how it scales, and the underlying infrastructure's capabilities. This is where you specify if you want to use a Windows or Linux host, instance size, and scaling capabilities.

    2. Web App: The App Service/Web App is the actual host for your application code which can be deployed as code or Docker containers. The choice between code and containers will depend on your application setup and requirements.

    3. Application Insights (Optional): For monitoring, you can integrate Azure Application Insights to gather telemetry and metrics from your application, providing visibility into the performance and usage patterns.

    4. Authentication and Authorization (Optional): To secure your application and manage access, you can configure the App Service's built-in authentication and authorization features.

    Now, let's set up a basic Pulumi program in Python to create an App Service Plan and a Web App for serving an AI model. This example will create a Linux App Service Plan and deploy a sample Python Flask application container that could serve your trained AI model.

    import pulumi from pulumi_azure_native import resources, web # Create an Azure Resource Group resource_group = resources.ResourceGroup('ai_model_resource_group') # Create an App Service Plan app_service_plan = web.AppServicePlan('ai_model_service_plan', resource_group_name=resource_group.name, location=resource_group.location, kind='Linux', # Defines the kind of App Service Plan (Linux or Windows) reserved=True, # This must be true for Linux sku=web.SkuDescriptionArgs( tier='Basic', # Define the pricing tier (Free, Basic, Standard, Premium, etc.) name='B1', # Define the SKU name for the pricing tier size='B1', # The size of the App Service Plan family='B', # The family of the App Service Plan (B = Basic) capacity=1 # The number of workers for the App Service Plan ) ) # Create a Web App web_app = web.WebApp('ai_model_web_app', resource_group_name=resource_group.name, location=resource_group.location, server_farm_id=app_service_plan.id, # Link the Web App to our App Service Plan https_only=True, # Force the use of HTTPS for added security site_config=web.SiteConfigArgs( app_settings=[ # Define environment variables for your application web.NameValuePairArgs(name='WEBSITES_PORT', value='80'), # The port your app runs on # Additional settings can be added as needed ], linux_fx_version='PYTHON|3.8' # Define the runtime stack for Linux-based App Services # You will add your container details here if deploying a custom container ) ) # Optional: Set up Application Insights for telemetry and monitoring # app_insights = insights.Component( ... ) # Export important attributes pulumi.export('app_service_plan_id', app_service_plan.id) pulumi.export('web_app_endpoint', web_app.default_host_name.apply( lambda host_name: f'https://{host_name}')) # Export the Web App's endpoint URL


    • We first set up a new resource group to contain and manage the Azure resources we're deploying.
    • We then define an App Service Plan (app_service_plan), specifying that we want to use Linux (kind='Linux'), set it up with the capacity for basic usage (tier='Basic' and name='B1'), and keep it reserved for our use.
    • Next, we provision a Web App (web_app), providing the ID of the App Service Plan we created (server_farm_id=app_service_plan.id) and configuring it to only use HTTPS for secure communication (https_only=True).
    • Within the site_config of the Web App, we specify the application settings and the runtime stack, which in this case is a Python 3.8 runtime. If you are deploying a Docker container with a model server (e.g., using MLflow or TensorFlow Serving), this is where you would specify the container settings.
    • Finally, we export the ID of the App Service Plan and the endpoint of the Web App, which will be the URL you use to send inference requests to your model.

    After you have your model packaged appropriately (e.g., a Flask app serving your model), you can deploy it to the Web App through various methods, such as continuous deployment from a source control repository or pushing a Docker container image to Azure Container Registry or Docker Hub.

    Remember to replace any placeholders or sample values with the actual values for your specific use case. For instance, you need to replace 'YOUR_DOCKER_IMAGE' with the Docker image URL of your AI model application if you choose to deploy a containerized app.