Serverless API for AI Model Serving with AWS API Gateway

Question

Pulumi · Accepted Answer

To create a serverless API for AI model serving using AWS API Gateway, we'll utilize various AWS services, including AWS Lambda to run the AI model code, and AWS API Gateway to manage and expose the API. This combination allows you to serve your AI model endpoints without managing any servers, while providing capabilities like scaling, security, and monitoring.

Here are the steps we'll take to implement the serverless API:

1. **Create an AWS Lambda Function**: This function will contain the code for your AI model. It will be triggered by API Gateway whenever a request to the API is made.
2. **Define an API Gateway**: This will act as the front door to your API, routing incoming requests to the appropriate backend, such as our Lambda function.
3. **Create API Gateway Resources and Methods**: These are the individual endpoints of your API, such as `/predict` for an AI model prediction. Methods are the HTTP methods (GET, POST, etc.) you'll allow on these endpoints.
4. **Deploy the API**: AWS API Gateway requires a deployment to access the defined resources and methods outside of AWS. We will also create a stage which is a snapshot of the API we want to deploy.
5. **Setup Request and Response Integrations**: These define how API Gateway transforms requests before sending them to Lambda, and how it transforms the responses before returning them to the client.

Let's implement these steps in Pulumi using Python.

```python
import pulumi
import pulumi_aws as aws

# Define the role and policy for AWS Lambda that allows logging to CloudWatch.
lambda_role = aws.iam.Role("lambdaRole", assume_role_policy=json.dumps({
    "Version": "2012-10-17",
    "Statement": [{
        "Action": "sts:AssumeRole",
        "Effect": "Allow",
        "Principal": {"Service": "lambda.amazonaws.com"},
    }]
}))

lambda_policy_attachment = aws.iam.RolePolicyAttachment("lambdaPolicyAttachment",
    role=lambda_role.name,
    policy_arn="arn:aws:iam::aws:policy/service-role/AWSLambdaBasicExecutionRole"
)

# Create a Lambda function that will contain the logic for our AI model.
# Make sure to package your AI model code and dependencies in `ai_model.zip`
# This lambda function will execute model inference based on the input.
ai_model_lambda = aws.lambda_.Function("aiModelLambda",
    code=pulumi.AssetArchive({"ai_model.zip": pulumi.FileArchive("./ai_model.zip")}),
    role=lambda_role.arn,
    handler="handler.main",  # 'handler' is the filename; 'main' is the function.
    runtime="python3.8"  # Choose the appropriate runtime for the AI model.
)

# Create an API Gateway to expose the serverless API
api = aws.apigatewayv2.Api("apiGateway",
    protocol_type="HTTP",   # "HTTP" or "WEBSOCKET"
    route_key="POST /predict",  # Defining one route as an example.
)

# Create an integration between the API Gateway and the Lambda function.
# This includes defining how requests and responses are handled.
integration = aws.apigatewayv2.Integration("apiLambdaIntegration",
    api_id=api.id,
    integration_type="AWS_PROXY",  # Use AWS_PROXY type for Lambda integrations.
    integration_uri=ai_model_lambda.invoke_arn,
    payload_format_version="2.0",  # Specifies the format of the payload. 2.0 for HTTP APIs.
)

# Deploy the API Gateway. Without a deployment, the changes won't be visible publicly.
deployment = aws.apigatewayv2.Deployment("apiGatewayDeployment",
    api_id=api.id,
)

# Create a stage. It's like a named reference to a deployment, which supports lifecycle management (like rolling back).
stage = aws.apigatewayv2.Stage("apiGatewayStage",
    api_id=api.id,
    deployment_id=deployment.id,
    name="prod"  # Use an appropriate stage name.
)

# Expose the URL endpoint as a stack output
pulumi.export("api_endpoint", api.api_endpoint)
```

In this program:

- We start by defining an IAM role and attaching policies that will allow our Lambda function to log to AWS CloudWatch.
- Then we create a Lambda function with the `pulumi_aws.lambda_.Function` class. Ensure your AI model code, including the handler and dependencies, is zipped and specified in the `code` constructor argument.
- We then create an API Gateway using `pulumi_aws.apigatewayv2.Api` to expose our Lambda function.
- An integration is defined using `pulumi_aws.apigatewayv2.Integration`, which connects the API Gateway to our Lambda function, with configurations that specify how requests and responses are handled.
- A deployment is created with `pulumi_aws.apigatewayv2.Deployment` so that our changes made to the API Gateway resources become live.
- Finally, a stage is defined using `pulumi_aws.apigatewayv2.Stage`, specifying the named deployment to be used, enabling lifecycle management such as updates or rollbacks.

Ensure your Pulumi stack is set up with the appropriate AWS credentials. Deploy the stack by running `pulumi up`, and once deployed, the endpoint of your serverless API will be outputted, ready for you to integrate with your application.