Integrating DynamoDB and CloudWatch

DynamoDB & CloudWatch in ML Pipeline

In the final step of the pipeline, we will integrate Amazon DynamoDB to store metadata of inference requests and model information, and use Amazon CloudWatch to monitor logs, measure performance, and generate alerts when problems occur.

This is an important step to ensure the pipeline can operate sustainably in a real environment.

🗃️ DynamoDB – Storing metadata inference

Amazon DynamoDB is a high-performance, auto-scalable, serverless NoSQL database. In this project, DynamoDB will be used to:

  • Store inference results from Lambda (input, output, time).
  • Save model information such as version, endpoint name.
  • Serve performance monitoring and analysis later.

📊 CloudWatch – Pipeline monitoring and analysis

Amazon CloudWatch is a central monitoring service on AWS. It will help you:

  • Monitor logs from Lambda and SageMaker Endpoint.
  • Create metric filters to analyze the number of inferences, errors, and delays.
  • Configure alarm when the pipeline has problems.

📚 Contents

📌 Summary

  • ✅ You will learn how to create and manage a DynamoDB table to store inference results.
  • ✅ Lambda will be extended to write data each time a prediction is made.
  • ✅ CloudWatch will help you monitor logs, analyze performance, and create alerts.

🎯 Outcomes after this chapter:

  • A complete ML pipeline capable of historical inference, automatic monitoring, and early warning when problems occur.

  • Ready to operate in production environments with easy scalability and maintenance.