1. Rapid Deployment of AI Updates via AWS Lambda Layers


    AWS Lambda Layers is a feature that allows you to manage and share code across multiple Lambda functions. Instead of duplicating common components such as libraries or custom runtimes in every Lambda function, you can place them in a Layer. This Layer can then be included in your Lambda runtime, which makes it easier and faster to manage updates to shared code - this is extremely helpful in scenarios such as deploying AI model updates or shared libraries across multiple functions in a serverless architecture.

    Here's how to rapidly deploy AI updates via AWS Lambda Layers using Pulumi in Python:

    1. Create a Lambda Layer for Shared Code: You will define the shared library or AI model assets you wish to deploy as a Layer. This allows your Lambda functions to import this code without including it in their deployment packages.
    2. Create Lambda Function(s): You will define Lambda functions that will leverage the shared layer created above. These functions can be used to execute your AI inference code or any operations that depend on the shared code.

    Here's a Pulumi program in Python that demonstrates how to create a Lambda Layer and then use it in a Lambda function:

    import pulumi import pulumi_aws as aws # Assuming you have AI or shared library code located in the 'ai_library' directory. # Note: Your actual path and content will depend on the specifics of your AI models or shared requirements. ai_layer_code_path = './ai_library' # Define the lambda layer that includes the AI/shared code. This should be a zip file. ai_layer = aws.lambda_.LayerVersion("aiLayer", layer_name="ai-model-layer", code=ai_layer_code_path, compatible_runtimes=["python3.8"] # The runtime your Lambda function uses. Adjust as necessary. ) # Define a Lambda function that uses the layer. ai_lambda_function = aws.lambda_.Function("aiLambdaFunction", runtime="python3.8", # The runtime must match the supported runtimes defined in the Lambda Layer code=pulumi.FileArchive('./lambda_function'), # Replace with the path to your Lambda function's code handler="index.handler", # Adjust based on your function. Format: <FILENAME>.<HANDLER_FUNCTION> layers=[ai_layer.arn], # Reference the layer ARN here 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", }, }], }), ).arn # Create a role for the lambda function with necessary permissions. ) # Export the Lambda function's ARN so that you can easily locate it in the AWS console or use it in other stacks. pulumi.export('lambda_function_arn', ai_lambda_function.arn)

    In this program:

    • We first set up a LayerVersion resource with our AI or shared library code. The code is assumed to be located in a directory named ai_library. You need to change the code parameter to the path where your Layer's code is stored.
    • We then create a Function resource that represents our Lambda function. The runtime parameter is set to Python 3.8, but you should choose the runtime that matches your Layer's compatible runtimes.
    • This function specifies the Layer we created earlier by including the arn in the layers list.
    • We also create an IAM Role for the Lambda function with the necessary assume role policy that allows it to be invoked by AWS Lambda.

    Keep in mind, this is a basic illustration. Depending on your specific use case (such as the type of AI application you're deploying), you might need additional AWS services or resources set up (like API Gateway for HTTP endpoints, S3 for data storage, etc.).

    To run this Pulumi program, you will need Pulumi CLI installed and AWS credentials configured. Save this code in a file with a .py extension, and run pulumi up to deploy it. After deploying changes to the layer, all Lambda functions referencing it will need to be updated to the new LayerVersion to pull in the new code.