1. Operation-specific Analytics for AI Services with Azure API Management


    To accomplish the goal of setting up operation-specific analytics for AI services with Azure API Management, you'll need to create and configure various Azure resources. This includes creating an API Management instance, configuring an API that provides access to the AI services, and setting up analytics on the operations provided by that API.

    Here is a step-by-step guide to achieving this, together with a Pulumi Python program:

    1. Set up an Azure API Management service: This service will act as a facade for the AI services you want to expose, providing functionalities like request throttling, authentication, and analytics.

    2. Define the API and its operations: Define the structure of your API within the API Management service, including the operations that the API will provide. These operations could provide access to different AI functionalities, depending on your use case.

    3. Configure OpenID Connect Provider (Optional): If your AI services require authentication, you might need to configure an OpenID Connect Provider (OIDC) for securing access to the API operations.

    4. Set up analytics: Azure API Management provides in-built analytics functionality. However, you can also integrate it with Azure Application Insights or other monitoring services for deeper operation-specific analytics.

    Below is a Pulumi Python program that defines an Azure API Management instance and demonstrates how to declare an API and configure analytics. This example assumes that you have defined the AI services elsewhere and that you're providing a facade to those services via Azure API Management for operational analytics.

    import pulumi import pulumi_azure_native as azure_native # Create a resource group for organizing the resources in Azure resource_group = azure_native.resources.ResourceGroup("api-rg") # Create the API Management service where all the APIs will be hosted api_management_service = azure_native.apimanagement.ApiManagementService("api-service", resource_name="my-api-management-service", resource_group_name=resource_group.name, publisher_name="My Organization", publisher_email="contact@myorg.com", sku=azure_native.apimanagement.ServiceSkuPropertiesArgs( name="Developer", # Use "Developer" for testing and development capacity=1, ), location=resource_group.location, ) # Define an API that hooks into your AI services ai_api = azure_native.apimanagement.Api("ai-api", resource_name="my-ai-services", resource_group_name=resource_group.name, service_name=api_management_service.name, display_name="AI Services API", description="API for accessing AI operations", path="ai", protocols=["https"], ) # (Optional) Setup an OpenID Connect Provider if you are using OIDC for authentication # Note: Replace the placeholder values with your actual OIDC configuration details openid_connect_provider = azure_native.apimanagement.OpenIdConnectProvider("oidc-provider", resource_name="my-oidc-provider", resource_group_name=resource_group.name, display_name="My OIDC Provider", service_name=api_management_service.name, client_id="your-client-id", client_secret="your-client-secret", metadata_endpoint="https://your-oidc-provider/.well-known/openid-configuration", ) # Finally, export the API Management service endpoint for easy access pulumi.export("api_management_endpoint", api_management_service.gateway_url)

    In this program:

    • We create an Azure API Management service which is going to be the main component where we declare our APIs.
    • An API instance is defined with the name ai-api, representing the AI services' API. We specify parameters such as the display name, the API's endpoint (path), and the protocols it supports.
    • Optionally, if your AI service requires user authentication, we set up an OpenID Connect Provider with necessary details like the client ID and metadata endpoint. Replace the dummy values with the actual configuration details of your OIDC provider.
    • We then export the endpoint of the API Management service to easily access it later.

    Please note that integrating analytics in specific operations requires setting up monitoring and logging features on these Azure resources. This area is quite vast and depends heavily on the specific AI services being instrumented and the specific analytics required. For detailed operation-specific analytics, you may explore enabling diagnostic settings on the API Management service to send data to Azure Monitor or Application Insights, where you can analyze the metrics and logs.

    For more detail on Azure API Management and the analytics capabilities it offers, refer to the Azure API Management documentation.