This course gives you a hands-on path to mastering FastAPI, the modern Python framework for building lightning-fast APIs. You’ll start by understanding what APIs are, how HTTP methods work, and how to set up your first FastAPI project.

From there, you’ll dive deeper into real-world development—learning to work with path and query parameters, POST requests, and data validation using Pydantic. You’ll then move on to advanced concepts like PUT and DELETE operations, improving API performance, and integrating machine learning models into production-ready endpoints.

The course also includes a complete guide to Dockerizing FastAPI applications and deploying them on AWS, giving you end-to-end experience in building, containerizing, and deploying ML-powered APIs.

What you will learn:

• Fundamentals of APIs and HTTP methods
• Setting up and running FastAPI applications
• Handling parameters and request bodies
• Data validation with Pydantic
• Serving and optimizing ML models with FastAPI
• Using Docker for scalable ML deployments
• Deploying FastAPI APIs to AWS

By the end of this course, you’ll be able to build and deploy your own production-grade ML APIs using FastAPI with confidence.