Serverless Machine Learning Deployment with AWS API Gateway

Getting a machine learning model to make predictions in a notebook is one thing, but making it accessible to the world is where the real value lies. This text-based course guides you through the process of turning your trained models into production-ready serverless APIs. You will transition from local model development to cloud-based deployment, learning how to wrap models in lightweight serverless functions and expose them securely to external applications to handle real-world prediction requests efficiently. What you will learn: Understand the fundamentals of serverless architecture, REST APIs, and how AWS Lambda interacts with API Gateway; Configure AWS Lambda functions to load machine learning models and run inference on incoming data; Create and secure REST API endpoints using API Gateway to handle POST requests; Apply modern optimization techniques, such as managing Lambda cold starts and optimizing package sizes; Implement basic API security, monitoring, and model versioning to keep your deployment robust. The course begins with foundational concepts of cloud architecture and API design before moving into step-by-step written explanations and structured code exercises to deploy your first model. Designed for beginner data scientists, software developers, and cloud enthusiasts, this course requires no prior cloud infrastructure experience. Start reading today to bridge the gap between machine learning development and cloud deployment.