1. Linking Azure Functions to Azure Machine Learning for Inference


    To link Azure Functions to Azure Machine Learning for inference, you can follow a process that involves several steps.

    Firstly, you would deploy an Azure Machine Learning Workspace and an inference endpoint using Azure Machine Learning services. The inference endpoint is essentially a web service that exposes your ML model for inference. Then, you would set up an Azure Function app which acts as a serverless compute service. The function you deploy will receive data, send it to your AML inference endpoint for processing, and receive the predictions to return as a response.

    Here is a step-by-step guide, along with the corresponding Pulumi program in Python, that demonstrates how to set up this architecture:

    1. Define an Azure Resource Group: This serves as a logical container in which you will deploy and manage the Azure resources for your application.

    2. Set up an Azure Machine Learning Workspace: The workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models with Azure Machine Learning.

    3. Create an Inference Endpoint: This is where your Machine Learning model will be hosted for inferencing. An endpoint is created within the Azure Machine Learning workspace context.

    4. Deploy an Azure Functions App: You need to create a Functions App which is the environment where your function code runs. The function will invoke the inference endpoint.

    5. Implement the Function: You will implement the Azure Function code. This code isn’t provided by Pulumi; you would develop it in your language of choice supported by Azure Functions. It's responsible for handling the HTTP requests, querying the AML endpoint, and returning the response.

    6. Deploy the Function Code: Using Pulumi, you will deploy the code for your function to Azure.

    Let’s write a Pulumi program that sets up an Azure Functions app with a connection to the Azure Machine Learning inference endpoint (note: this is a simplified version, and in production, you may need additional security measures, environment variables, and error handling):

    import pulumi import pulumi_azure_native as azure_native # Define an Azure resource group resource_group = azure_native.resources.ResourceGroup('ai-inference-rg') # Create an Azure Machine Learning workspace ml_workspace = azure_native.machinelearningservices.Workspace( 'ml-workspace', resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.machinelearningservices.SkuArgs(name='Basic'), identity=azure_native.machinelearningservices.IdentityArgs(type='SystemAssigned'), ) # Deploy an Azure Functions App with its appsettings function_app = azure_native.web.WebApp( 'function-app', resource_group_name=resource_group.name, kind='functionapp', location=resource_group.location, server_farm_id="/subscriptions/{subscription_id}/resourceGroups/{rg_name}/providers/Microsoft.Web/serverfarms/{app_service_plan_name}", site_config=azure_native.web.SiteConfigArgs( app_settings=[ azure_native.web.NameValuePairArgs(name="FUNCTIONS_WORKER_RUNTIME", value="python"), # Define other required appsettings # For example, the below setting can have details to authenticate # and interact with the Azure Machine Learning endpoint # azure_native.web.NameValuePairArgs( # name="AML_ENDPOINT", # value="your_aml_endpoint_url" # ), ], ), ) # Deploy your function code (assumes you have the code package ready) # For example, the app content can be deployed from a URL app_code = azure_native.web.WebAppSourceControl( 'app-code', resource_group_name=resource_group.name, name=function_app.name, is_manual_integration=True, repo_url="https://github.com/your_username/your_repo_name.git", branch="main", ) # Here you would add the code or mechanism to establish scaffolding for interaction # between the function application and the machine learning inference endpoint. # Export the Azure Function App URL pulumi.export('function_app_url', function_app.default_host_name.apply( lambda hostname: f"https://{hostname}" ))

    This program performs the following:

    • Creates a resource group to house our resources.
    • Sets up an Azure Machine Learning workspace where our machine learning models will be managed and deployed. Here, we use system-assigned managed identity for authentication, and we’ve chosen a basic SKU.
    • Deploys an Azure Functions app. In the app settings, we specify the runtime (Python in this case) and any other relevant settings (like the AML inference endpoint URL, if necessary).
    • Deploys the function code from a Git repository. You will need to replace the placeholders with your actual repository URL and branch name.

    Remember that this Pulumi program doesn't define the actual ML model deployment or the function logic; you need to implement the function code separately and make sure it interacts with the ML model endpoint correctly. Also, ensure you have the necessary permissions and networking configurations in place for the function app to reach the ML workspace.

    After deploying this infrastructure with Pulumi, you'll have an Azure Functions app configured and ready to send data to your Azure Machine Learning model for inference.