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Workshop: Building an End-to-End Machine Learning Pipeline on AWS with Lambda, API Gateway, S3, SageMaker & DynamoDB

Overview

In this workshop, you will learn how to build and deploy an end-to-end machine learning pipeline on AWS.
We will use AWS Lambda for preprocessing and inference, API Gateway to expose RESTful endpoints, S3 for data storage, Amazon SageMaker for model training and hosting, DynamoDB for metadata storage, and CloudWatch for monitoring and logging.

Workshop Architecture

🎯 Objectives

  • Understand how to design and deploy a complete ML pipeline on AWS.
  • Build and configure data ingestion, preprocessing, and model training workflows.
  • Deploy and manage machine learning models with Amazon SageMaker.
  • Expose inference endpoints through API Gateway and Lambda.
  • Integrate DynamoDB for model metadata and use CloudWatch for monitoring.
  • Learn best practices for permissions, security, and cost optimization.

🧰 Requirements

  • An existing AWS account (Free Tier: https://aws.amazon.com/free)
  • Basic knowledge of Python or Go (for Lambda functions and ML scripts)
  • Familiarity with REST APIs and JSON
  • Tools: AWS CLI, Git, Docker (optional), and a web browser
  • (Optional) Postman for testing inference endpoints

💡 If you already have an AWS account with full access, you can skip IAM setup and continue directly to building resources.

📚 Contents

  1. Introduction
  2. Check-AWS-Account-and-Permissions
  3. Create-S3-Bucket-for-Data-Storage
  4. Implement-Lambda-Preprocessing-Function
  5. Train-and-Register-Model-with-Amazon-SageMaker
  6. Deploy-SageMaker-Endpoint-for-Inference
  7. Build-Lambda-Inference-Function-and-API-Gateway
  8. Integrate-DynamoDB-and-CloudWatch
  9. Clean-Up-Resources
  10. Conclusion-and-Key-Takeaways