1. Autoscaling Azure Functions for ML Model Serving


    To autoscale an Azure Function for machine learning (ML) model serving, you would typically create an Azure Function App that can run serverless functions to serve your ML model predictions. Azure Functions support autoscaling by default, which means they can automatically adjust the number of compute resources allocated based on the demand.

    Below is an example program in Python using Pulumi to create an Azure Function App that could be used for ML model serving. This program uses the azure_native package, which allows you to interact directly with Azure resources in a native manner.

    Before you begin, you will need to have the Pulumi CLI installed and configured for Azure, and you should have already logged in to your Azure account via the Azure CLI using the az login command.

    The program performs the following actions:

    • Sets up an Azure resource group.
    • Creates a storage account required for storing the function code and state.
    • Defines an app service plan. While Azure Functions can run in a consumption plan (serverless), we define a dedicated plan to demonstrate autoscaling features.
    • Creates an Azure Function App which is the container for our functions.
    • Configures the function app with an Application Insights instance for monitoring.

    Here's the program:

    import pulumi import pulumi_azure_native as azure_native from pulumi_azure_native import web # Create an Azure Resource Group resource_group = azure_native.resources.ResourceGroup('resource_group') # Create an Azure Storage Account account = azure_native.storage.StorageAccount('storageaccount', resource_group_name=resource_group.name, kind="StorageV2", sku=azure_native.storage.SkuArgs(name="Standard_LRS") ) # Create an Application Insights instance for monitoring app_insights = azure_native.insights.Component('appInsights', resource_group_name=resource_group.name, kind="web", application_type=azure_native.insights.ApplicationType.WEB ) # Create an App Service Plan with autoscale settings plan = web.AppServicePlan('serviceplan', resource_group_name=resource_group.name, sku=web.SkuDescriptionArgs( tier="Standard", name="S1" # You can choose different SKU levels based on your needs ), kind="FunctionApp", reserved=False # This must be false for plans not running on Linux workers ) # Create the Function App function_app = web.WebApp('functionapp', resource_group_name=resource_group.name, server_farm_id=plan.id, site_config=web.SiteConfigArgs( app_settings=[ web.NameValuePairArgs(name="FUNCTIONS_WORKER_RUNTIME", value="python"), # Assuming Python runtime for the ML model web.NameValuePairArgs(name="FUNCTIONS_EXTENSION_VERSION", value="~3"), web.NameValuePairArgs(name="APPINSIGHTS_INSTRUMENTATIONKEY", value=app_insights.instrumentation_key), web.NameValuePairArgs(name="AzureWebJobsStorage", value=account.primary_connection_string.apply(lambda c: c)), web.NameValuePairArgs(name="WEBSITE_RUN_FROM_PACKAGE", value="1"), # We package the function app as a zip file ], ), https_only=True, client_cert_enabled=False, ) # Export the Function App URL pulumi.export('endpoint', function_app.default_host_name.apply(lambda name: f"https://{name}/api"))

    In this program:

    • We are creating the necessary infrastructure for Azure Functions, with a focus on the resource group, storage account, application insights for monitoring, an app service plan, and the function app itself.
    • The azure_native.resources.ResourceGroup class represents a new Azure Resource Group where all resources are created.
    • The azure_native.storage.StorageAccount class represents a new Azure Storage Account, which is a prerequisite for Azure Functions to store the code and execution state.
    • The azure_native.insights.Component class sets up Application Insights, which is used for monitoring the performance of the Azure Function.
    • The web.AppServicePlan class creates a service plan for the Azure Function, allowing us to define autoscale settings if necessary.
    • The web.WebApp class creates the actual Azure Function App, where you would deploy your Python-based ML model.

    To complete this setup:

    • You would package your ML model and the function code into a zip file, which you can then deploy to the Azure Function App.
    • You should update WEBSITE_RUN_FROM_PACKAGE with the appropriate location of your package.
    • Custom autoscaling rules could be applied by updating the SKU settings within the App Service Plan.

    Remember to package your ML model along with a Python function in a deployable format, so you can then update the WEBSITE_RUN_FROM_PACKAGE app setting to point to the package location.

    To scale up the service plan, you would typically adjust the SKU settings or enable autoscaling settings within the Azure Portal or by using Azure CLI commands. In Pulumi, you would adjust the sku parameter within the web.AppServicePlan to reflect the desired pricing tier and size that supports autoscale.

    It is also possible to configure autoscale settings within the web.AppServicePlan by setting the maximum_elastic_worker_count for a Premium Elastic App Service Plan to allow for scaling out during high load. However, this is beyond the scope of the current example. For a production system, you would need to consider these settings carefully based on the expected load and cost considerations.