Asynchronous ML Model Training with AWS Lambda Invocation

Imagine being tasked with the challenge of training machine learning (ML) models in a serverless environment at work. Your boss expects a fast, efficient, and scalable solution to this problem. A key requirement is for the primary Lambda function to return a response immediately after triggering the model training process, without waiting for the training to complete. Sounds like a tall order, doesn't it? Fortunately, AWS Lambda has got your back. In this article, we'll explore how Lambda invocation can be utilized to solve this issue by asynchronously calling another Lambda function using Python. We'll also demonstrate how the child function can receive data from the parent function.

Why Use Lambda Invocation?

AWS Lambda is a serverless compute service that allows you to run your code without provisioning or managing servers. Lambda invocation is a powerful feature that enables one Lambda function to call another, allowing you to offload tasks, process events in parallel, and set up complex workflows. In our case, invoking another Lambda function to train ML models enables the primary Lambda function to return a response immediately, ensuring that the main process doesn't get blocked waiting for the training job to finish.

How to Invoke a Lambda Function with Python

To invoke a Lambda function from another, you'll first need the AWS SDK for Python, Boto3. Make sure you have it installed by running:

pip install boto3

Next, you need to set up the necessary permissions to allow the parent Lambda function to call the child Lambda function. In the AWS Management Console, navigate to the IAM role assigned to the parent Lambda function and attach the AWSLambdaRole policy.

Now let's create a simple parent Lambda function that will invoke the child Lambda function. The parent function will use Boto3's invoke method to make an asynchronous call to the child function.

import boto3
import json

def lambda_handler(event, context):
    lambda_client = boto3.client('lambda')

    # Replace 'child-function-name' with the actual name of your child Lambda function
    response = lambda_client.invoke(
        FunctionName='child-function-name',
        InvocationType='Event', # Asynchronous invocation
        Payload=json.dumps(event)
    )

    return {
        'statusCode': 200,
        'body': 'Model training job started.'
    }

Here's a sample child Lambda function that trains an ML model using the data passed from the parent function:

import json

def lambda_handler(event, context):
    # Load data from the event object sent by the parent Lambda function
    training_data = json.loads(event['body'])

    # Train your ML model here using the training_data
    # ...

    return {
        'statusCode': 200,
        'body': 'Model training completed.'
    }

In this example, the parent Lambda function sends the input event as a payload to the child function, which then extracts the data and uses it for training the ML model.

Conclusion

As demonstrated in this article, using AWS Lambda invocation to asynchronously call another Lambda function is a powerful technique that can solve complex use cases such as initiating ML model training without waiting for the process to complete. By leveraging this approach, you can achieve faster response times, better scalability, and an efficient serverless architecture for your applications. The Python examples provided serve as a solid foundation to build upon for your specific use case, allowing you to focus on the core logic of your application rather than infrastructure management.