1. Global API Gateway for ML Models with AWS CloudFront


    To create a global API Gateway for serving machine learning (ML) models, you can use AWS services like API Gateway to handle API requests and AWS CloudFront to deliver content globally with low latency. Below you will find an explanation of the program along with the Pulumi code to set up the required infrastructure.

    First, let's break down the steps we'll be taking:

    1. Create a REST API using AWS API Gateway. This will serve as the endpoint for your ML models.
    2. Configure a CloudFront distribution. This will speed up the delivery of your API responses by caching the content at edge locations closer to your users.
    3. Set up the necessary integration between API Gateway and CloudFront.

    Here's the Pulumi program to set this up in Python:

    import pulumi import pulumi_aws as aws # Step 1: Create an API Gateway to serve the ML models. # The 'rest_api' defines the root resource, under which all your API endpoints will exist. rest_api = aws.apigateway.RestApi("mlModelApi", description="API Gateway for ML Models") # Here, you can define the individual API endpoints and methods (like GET, POST, etc.) # For example, you might have a POST method to submit data to your ML model for inference. # Step 2: Configure AWS CloudFront. # The 'cloudfront_distribution' resource will create a content distribution network. # It will serve as a front for your API Gateway to ensure low-latency delivery globally. cloudfront_distribution = aws.cloudfront.Distribution("cloudfrontForApiGateway", enabled=True, # Using the API Gateway domain name as your origin. origins=[aws.cloudfront.DistributionOriginArgs( domain_name=rest_api.deployment_invoke_url.apply(lambda url: url.split('/')[2]), # Extract the domain from invoke URL. origin_id="apiGatewayOrigin", # Using custom headers to authenticate CloudFront requests to API Gateway. custom_headers={ "X-Api-Key": "your-api-key" # Replace with your actual API key or authentication mechanism. } )], default_cache_behavior=aws.cloudfront.DistributionDefaultCacheBehaviorArgs( allowed_methods=["GET", "HEAD", "OPTIONS", "PUT", "POST", "PATCH", "DELETE"], cached_methods=["GET", "HEAD"], target_origin_id="apiGatewayOrigin", # Setting up caching behavior, including TTL values and forwarding headers/query strings. forwarded_values=aws.cloudfront.DistributionDefaultCacheBehaviorForwardedValuesArgs( query_string=True, # Forward query strings to the API Gateway. headers=["Authorization"] # List any headers that CloudFront needs to forward to your endpoint. ), viewer_protocol_policy="redirect-to-https", # Redirect HTTP requests to HTTPS. min_ttl=0, default_ttl=3600, # Default cache duration (in seconds) for resources. max_ttl=86400, ), # Specifying the viewer certificate to enable SSL/TLS. viewer_certificate=aws.cloudfront.DistributionViewerCertificateArgs( cloudfront_default_certificate=True ), # The REST API will need to be deployed for CloudFront to access it. # In a real-world scenario, you'd likely handle deployment with a CI/CD system. ) # Step 3: Exporting the CloudFront distribution domain. # This is the domain name you will give to your users to access the ML API globally. pulumi.export("cloudfront_domain_name", cloudfront_distribution.domain_name)


    • The aws.apigateway.RestApi resource creates an Amazon API Gateway REST API, which you'll use to deploy your machine learning models as endpoints.
    • In the aws.cloudfront.Distribution resource, the origins argument specifies the domain name of your API Gateway as the origin of the content. Note that the domain_name is extracted from the API Gateway deployment invoke URL, which you obtain by deploying your API.
    • We use default_cache_behavior in CloudFront to manage how requests to your API Gateway are cached. We've enabled all RESTful methods, specified that we want to cache GET and HEAD requests, and set viewer_protocol_policy to always use https.
    • The viewer_certificate is set to use the CloudFront default SSL/TLS certificate to encrypt the content delivery.
    • Finally, we export the CloudFront distribution domain name as an output. This is the URL you will distribute to consumers of your ML API.

    Please note that for production deployment, you may need to adjust caching settings and customize error responses or add WAF (Web Application Firewall) protection to secure your API. Additionally, you might use more complex authentication mechanisms, rather than a single X-Api-Key header shown above.