1. Real-time Inference with Azure Functions for AI Models


    To set up real-time inference with Azure Functions for AI models, you will typically deploy an Azure Function App that is able to run your machine learning code. Azure Functions provides a serverless environment, which allows you to run your code without worrying about the underlying infrastructure. For AI models, you might use an HTTP-triggered function that receives data for inference and returns the prediction.

    In this Pulumi program, we'll create a simple Azure Function App with Python support. The function will be set up for HTTP trigger, which means it can receive requests over HTTP and you can send inference data within these requests. Here's how you can achieve this:

    1. Define a resource group to organize related resources.
    2. Create an Azure Storage Account, which is required by Azure Functions to manage state and other information.
    3. Create an Azure App Service Plan, which defines the execution context for the Azure Function. We will use a Consumption Plan, which is serverless and scales automatically.
    4. Set up the Azure Function App itself, which hosts the execution environment for the function code.
    5. Define the source code for the function, which you would typically obtain from your version control system or local files.
    6. Publish the function code to the Azure Function App.

    Below is the detailed Pulumi program written in Python:

    import pulumi import pulumi_azure as azure # Create an Azure Resource Group to organize resources within it resource_group = azure.core.ResourceGroup("ai-inference-rg") # Create an Azure Storage Account required by the Function App storage_account = azure.storage.Account("inferencestorage", resource_group_name=resource_group.name, account_tier="Standard", account_replication_type="LRS") # Create an App Service Plan which defines the context of running the Function App app_service_plan = azure.appservice.Plan("inference-plan", resource_group_name=resource_group.name, kind="FunctionApp", sku={ "tier": "Dynamic", "size": "Y1" # This specifies a consumption plan }) # Create the Function App function_app = azure.appservice.FunctionApp("real-time-inference", resource_group_name=resource_group.name, app_service_plan_id=app_service_plan.id, storage_account_name=storage_account.name, storage_account_access_key=storage_account.primary_access_key, app_settings={ "FUNCTIONS_WORKER_RUNTIME": "python" # Specify the runtime as Python for AI model inference code }) # For this example, let's assume our function's code is in "inference_function.py" in the current directory # In real-world scenarios, you might retrieve this from a repository or a local directory source_code = pulumi.FileArchive("./functions") # Publish the function code code_blob = azure.storage.Blob("inference-code", resource_group_name=resource_group.name, storage_account_name=storage_account.name, storage_container_name="$web", # This is the default container name used by Azure Functions type="Block", source=source_code) # Export the Function App's default hostname so you can access it once it is deployed pulumi.export("endpoint", function_app.default_hostname.apply(lambda hostname: f"https://{hostname}/api/handler"))

    Here's a step by step explanation of the program:

    1. We import the pulumi and pulumi_azure modules to work with Pulumi and Azure resources.
    2. A new resource group ai-inference-rg is created to hold all related resources for this deployment.
    3. An Azure Storage Account inferencestorage is provisioned, which the Azure Functions will use for managing triggers, logging, and other operational data.
    4. An Azure App Service Plan inference-plan is created, here we specify it to be serverless and dynamic, implying it scales as necessary and we pay only for the compute resources we consume.
    5. The Azure Function App real-time-inference is configured with the previously created app service plan and storage account.
      • The app_settings dictionary sets the runtime to Python, as our machine learning model inference code will be written in Python.
    6. We deploy our function code by packaging it into a FileArchive and creating a Blob in a special $web container on the storage account.
    7. Finally, we export the endpoint of the function app so that you can call your function over HTTP once it's deployed.

    To deploy your AI model as Azure Function code, you would package your model and its dependencies within the ./functions directory alongside an __init__.py file that defines the HTTP-triggered function. It's not included here but that code will take the inference data from HTTP requests and return predictions using the model.

    Once you run this Pulumi program, you'll have a fully-functional HTTP API endpoint backed by an Azure Function that you can use for making real-time inferences with your AI model.