1. Centralized AI Service Gateway with Azure API Management


    In order to create a centralized AI service gateway using Azure API Management (APIM), you can manage APIs that provide AI services. APIM allows you to present a unified set of APIs to your users, whether they are internal to an organization or external consumers.

    Azure API Management is a fully managed service that enables customers to publish, secure, transform, maintain, and monitor APIs. Setting up an API management includes creating an instance of the API Management service, defining or importing API schema, creating products to bundle APIs, setting up policies like quotas or transformations, and potentially setting up a developer portal for API documentation and testing.

    Here's a Pulumi Python program to set up a basic Azure API Management instance. This program will create an APIM service along with a demo API and product, which could be the starting point of your centralized AI gateway.

    import pulumi import pulumi_azure_native as azure_native # Define API Management service. api_management_service = azure_native.apimanagement.Service( "aiServiceGateway", resource_group_name="your_resource_group", # Replace with your resource group name # The Service properties below are examples, you can adjust them according to your needs publisher_name="MyCompany", publisher_email="contact@mycompany.com", sku=azure_native.apimanagement.ServiceSkuPropertiesArgs( name="Developer", # The 'Developer' tier is good for testing, you should choose an appropriate tier for production capacity=1, ), location="West US", # Choose the region that is best for you ) # Define a demo API. api = azure_native.apimanagement.Api( "demoApi", resource_group_name="your_resource_group", service_name=api_management_service.name, display_name="Demo API", description="A demo API for testing AI services", path="demoapi", protocols=["https"], ) # Define a product to bundle APIs and manage their usage. product = azure_native.apimanagement.Product( "demoProduct", resource_group_name="your_resource_group", service_name=api_management_service.name, display_name="Demo Product", description="A product offering for testing APIs", terms="Terms of use for demo product", subscription_required=True, approval_required=False, published=True, ) # Adding the demo API to the demo product. product_api = azure_native.apimanagement.ProductApi( "demoProductApi", resource_group_name="your_resource_group", service_name=api_management_service.name, product_id=product.product_id, api_id=api.api_id, ) # Export the API Management service URL pulumi.export("api_management_service_url", api_management_service.gateway_url)

    Explanation of the above code:

    1. We import the required modules and define an API Management service using the azure_native.apimanagement.Service class. This is the central place where all APIs will be managed.
    2. We then set up a demo API using the azure_native.apimanagement.Api class. This is where you'd define your AI service's RESTful interface.
    3. A Product is created using azure_native.apimanagement.Product which bundles one or more APIs, allowing subscription and access control.
    4. The API is then associated with the Product using azure_native.apimanagement.ProductApi.
    5. Finally, we export the URL of the API Management service so it can be used to access the managed APIs.

    Keep in mind that this is a simplistic configuration to get you started. A real-world AI service gateway will likely have multiple APIs, more comprehensive products bundling those APIs, and custom policies to manage keys, quotas, transformations, and more.

    You will also need to replace "your_resource_group" with the exact name of your Azure Resource Group where you want to deploy these resources.

    To run this program:

    1. Set up Azure credentials for Pulumi to use.
    2. Create a new directory for your Pulumi code.
    3. Run pulumi new azure-python to create a new Pulumi project configured for Python with Azure.
    4. Replace the generated __main__.py content with the code above.
    5. Run pulumi up to deploy your infrastructure.

    This should get you started with Azure API Management as the cornerstone for offering centralized AI services.