Deploying and Debugging ML Microservices in Production

Moving a machine learning model from a notebook to a reliable production environment is one of the biggest challenges in modern software engineering. This text-based course bridges the gap between data science and DevOps by teaching you how to package and run models as scalable microservices. You will progress from understanding core microservice architecture to hands-on deployment and real-world debugging strategies, gaining the confidence to containerize your models, expose them via APIs, and monitor their performance in production. What you'll learn: - Understand the foundational architecture of machine learning microservices and production lifecycles. - Build robust APIs to serve model predictions using modern web frameworks. - Containerize machine learning applications for consistent deployment across environments. - Debug common production errors, from dependency conflicts to memory leaks. - Implement basic observability, logging, and performance monitoring to detect model drift. - Apply continuous integration principles to automate model deployment pipelines safely. We begin with essential terminology and the fundamentals of microservices before moving step-by-step through API creation, containerization, and active debugging techniques. You will learn through clear, written explanations and practical code examples designed for real-world application. This course is designed for aspiring ML engineers, data scientists, and developers who understand basic machine learning concepts and want to master the production deployment pipeline. Start your journey toward mastering production-ready machine learning deployment today.