Developing Production-Ready ML APIs with MLOps

Transitioning a machine learning model from a local notebook to a reliable, accessible production API is a critical step in any AI workflow. This text-based course guides you through the foundational concepts of MLOps and API development, helping you bridge the gap between data science and software engineering. By the end of this course, you will understand how to design clean API endpoints, package your models, and ensure your system is robust enough to handle real-world requests safely and efficiently. What you'll learn: - Understand the core principles of MLOps and the lifecycle of production machine learning systems. - Build high-performance web APIs for model inference using modern Python frameworks like FastAPI. - Containerize machine learning applications using Docker to ensure consistent deployment across environments. - Implement basic input validation and error handling to protect your APIs from unexpected data. - Apply foundational testing and monitoring techniques to track model performance and API health. We begin with essential terminology and design principles before guiding you through step-by-step written explanations and code snippets to build, containerize, and deploy your first machine learning API. This course is designed for aspiring ML engineers, developers, and data scientists who want to learn production deployment practices from the ground up, with no prior DevOps experience required. Start reading today to turn your offline models into production-ready web services.