Introduction to Model Serving: Deploying ML APIs with FastAPI and Docker

Moving a machine learning model from a local environment to a reliable production system requires a specialized set of tools and architectural patterns. Understanding how to serve models ensures your predictions are accessible, fast, and scalable for real-world applications. This text-based course guides you through the foundational concepts of model serving, teaching you how to build production-ready inference pipelines. You will learn to wrap models in efficient APIs, containerize them for consistent deployment, and optimize performance using modern standards. What you'll learn: - Understand core model serving architectures and the lifecycle of machine learning models in production. - Build high-performance, asynchronous web APIs using Python, FastAPI, and type hints. - Convert and optimize machine learning models using the ONNX framework for faster inference. - Containerize your application environments with Docker to ensure reliable deployments across any infrastructure. - Implement basic testing for your ML endpoints using pytest to guarantee API stability. - Apply modern MLOps principles to scale your serving systems and handle incoming traffic efficiently. The course begins with key terminology and foundational definitions of APIs and containerization before moving into practical implementation. You will progress through step-by-step written explanations and structured code snippets to build, package, and test your own model serving pipeline. This program is designed for aspiring ML engineers, data scientists, and software developers new to MLOps, with no prior DevOps experience required. Start reading today to bridge the gap between model training and production-ready deployment.