1. Secure Content Delivery for AI Model APIs with CloudFront


    To create a secure content delivery system for your AI Model APIs using CloudFront, you'll want to build an AWS infrastructure that leverages Amazon CloudFront's distribution system to serve your API securely and with low latency across the globe. CloudFront is a content delivery network (CDN) service that securely delivers data, videos, applications, and APIs to customers globally with low latency and high transfer speeds.

    Here's what needs to be done:

    1. Set up an API endpoint, which can be done using Amazon API Gateway. API Gateway will create a scalable and secure API that can be integrated with Lambda functions or other AWS services to run your AI model.
    2. Create an AWS S3 bucket to store your Lambda function code if you use Lambda.
    3. Write Lambda functions to perform AI model predictions, which will be invoked by API Gateway.
    4. Deploy a CloudFront Distribution to put your API behind a CDN.

    Below, you'll find a Pulumi program, written in Python, that will perform steps related to CloudFront and Lambda (steps 2, 3, and 4). For the API Gateway setup and integration (step 1), you would need to set that up separately through AWS or Pulumi aws.apigateway.

    Now, let's break down the Pulumi code:

    1. Create a Lambda function, referenced here by ai_model_lambda_function. This function will have the code for handling AI model predictions.
    2. Create an S3 bucket, where your Lambda's deployment package will reside.
    3. Create a CloudFront distribution with a default cache behavior. The cache behavior will have a Lambda function association, where we reference the ARN of the Lambda function you created previously.
    4. The viewer certificate field in the CloudFront distribution is set to use the default CloudFront certificate, but you can also customize it with your domain's SSL/TLS certificate.
    5. Export important information like the CloudFront distribution domain name, which is the URL through which end users will access your API.

    Let's go ahead with the code:

    import pulumi import pulumi_aws as aws # Assume the AI Model Lambda function has already been defined elsewhere with required IAM role setup # Replace `YourAIModelLambdaFunction` with your actual Lambda function's name or ARN ai_model_lambda_function = aws.lambda_.Function.get("ai_model_lambda_function", "YourAIModelLambdaFunction") # Create a new CloudFront distribution that points to your API Gateway cloudfront_distribution = aws.cloudfront.Distribution("ai_model_api_distribution", enabled=True, is_ipv6_enabled=True, default_root_object="index.html", origins=[aws.cloudfront.DistributionOriginArgs( origin_id="apiGatewayOrigin", domain_name=ai_model_lambda_function.invoke_arn, # Assuming API Gateway's invoke ARN. Adjust as needed. custom_origin_config=aws.cloudfront.DistributionOriginCustomOriginConfigArgs( origin_protocol_policy="https-only", http_port=80, https_port=443, ), )], default_cache_behavior=aws.cloudfront.DistributionDefaultCacheBehaviorArgs( allowed_methods=["GET", "HEAD", "OPTIONS", "PUT", "POST", "PATCH", "DELETE"], cached_methods=["GET", "HEAD"], target_origin_id="apiGatewayOrigin", forwarded_values=aws.cloudfront.DistributionDefaultCacheBehaviorForwardedValuesArgs( query_string=True, cookies=aws.cloudfront.DistributionDefaultCacheBehaviorForwardedValuesCookiesArgs( forward="none", ), ), viewer_protocol_policy="redirect-to-https", min_ttl=0, default_ttl=3600, max_ttl=86400, lambda_function_associations=[aws.cloudfront.DistributionDefaultCacheBehaviorLambdaFunctionAssociationArgs( event_type="origin-request", lambda_arn=ai_model_lambda_function.arn.apply(lambda arn: f"{arn}:$LATEST"), # Use the latest version of your Lambda function )], ), viewer_certificate=aws.cloudfront.DistributionViewerCertificateArgs( cloudfront_default_certificate=True, ), ) # Export the CloudFront distribution's domain name which can be used to access your API. pulumi.export('distribution_domain_name', cloudfront_distribution.domain_name)

    This code sets up a secure, scalable, and efficient way to serve your AI Model API through AWS CloudFront. You will need to create and configure your API Gateway and Lambda function outside of this code. If you would like to manage API Gateway with Pulumi as well, you can use Pulumi's aws.apigateway module to define your API endpoints, methods, integrations, and deployments.

    Remember that you'll have to configure AWS credentials correctly for Pulumi to manage your AWS resources, and ensure that the Lambda function has adequate permission to be executed by CloudFront.