1. Edge AI Inference with Fastly Compute@Edge


    Edge AI inference typically involves running machine learning models close to the source of data, such as IoT devices or in this case, at the edge of the network. This approach minimizes latency since the data doesn't have to travel far to a centralized server for processing. The result can be almost instantaneous predictions from the model, an essential feature for real-time applications.

    Fastly offers an edge computing platform known as Compute@Edge that allows you to run code at the network edge. While Fastly itself is not directly supported by Pulumi, you can use Pulumi to manage related cloud services and resources that work alongside Fastly.

    Since the query mentioned Fastly's Compute@Edge specifically for Edge AI inference but didn't provide a particular cloud provider, I will show you how to set up related services using AWS, which can store your machine learning model and serve it through an AWS Lambda function. This can be used in conjunction with Fastly, where Fastly would serve as the CDN to cache and deliver content and AWS would perform the backend computation.

    Let's create an AWS Lambda function with an associated API Gateway to invoke the function over the web. This function can then be connected with your Fastly service:

    import pulumi import pulumi_aws as aws # Define an IAM role and attach the AWS Lambda basic execution Role policy to it. lambda_role = aws.iam.Role("lambdaRole", assume_role_policy="""{ "Version": "2012-10-17", "Statement": [{ "Action": "sts:AssumeRole", "Effect": "Allow", "Principal": { "Service": "lambda.amazonaws.com" } }] }""") role_policy_attachment = aws.iam.RolePolicyAttachment("lambdaRoleAttachment", role=lambda_role.name, policy_arn=aws.iam.ManagedPolicy.AWS_LAMBDA_BASIC_EXECUTION_ROLE) # Create a ZIP archive of your machine learning model and inference code. # For this example, you assume the zip file is already created and available. # In practice, you would automate the zipping process as part of your CI/CD workflow. lambda_function_zip = pulumi.FileAsset("function.zip") # Create an AWS Lambda function. You'd replace the code with your model and inference logic. lambda_function = aws.lambda_.Function("edgeAiInferenceFunction", role=lambda_role.arn, handler="index.handler", # The entrypoint into your function code. runtime="python3.8", # Choose an appropriate runtime for your function. code=lambda_function_zip, timeout=30) # Optional: Set the timeout for the function. Adjust as necessary. # Expose the Lambda function via API Gateway to make it accessible over the web. api_gateway = aws.apigatewayv2.Api("apiGateway", protocol_type="HTTP") # Create a default route that invokes the Lambda function. integration = aws.apigatewayv2.Integration("apiGatewayIntegration", api_id=api_gateway.id, integration_type="AWS_PROXY", integration_uri=lambda_function.arn) # Define the HTTP methods for which this integration should be triggered. route = aws.apigatewayv2.Route("apiGatewayRoute", api_id=api_gateway.id, route_key="POST /infer", # Or any path that meets your requirements. target=pulumi.Output.concat("integrations/", integration.id)) # Deploy the API Gateway to make it serve requests. deployment = aws.apigatewayv2.Deployment("apiGatewayDeployment", api_id=api_gateway.id, # The dependency on the 'route' ensures the integration is created before the deployment starts. __opts__=pulumi.ResourceOptions(depends_on=[route])) # Create a stage, which is a named reference to a deployment. stage = aws.apigatewayv2.Stage("apiGatewayStage", api_id=api_gateway.id, deployment_id=deployment.id, name="prod") # Output the endpoint URL of the API Gateway stage. pulumi.export("endpoint_url", stage.invoke_url)

    In this program:

    1. We set up an IAM role for AWS Lambda with the necessary permissions.
    2. We define a Lambda function, assuming you have a ZIP file with the model and inference code.
    3. We create an AWS API Gateway integration to trigger the Lambda function over HTTP.

    To connect this AWS setup with Fastly, you would need to configure Fastly to route requests to this AWS API Gateway endpoint. Your Fastly service configuration would include backend settings pointing to the API Gateway URL, and any necessary caching rules or other logic you want to apply at the edge.

    Please note that while Fastly Compute@Edge allows you to run code at the edge, the connection between AWS Lambda and Fastly will involve data transfer over the internet. So, you'd use AWS for compute-heavy AI inference and use Fastly to cache the result at the edge, providing low-latency responses to end-users.

    The AWS components are fully managed with Pulumi, and changes to your cloud services can be applied through Pulumi's infrastructure as code framework.