MLOps Fundamentals: Building and Deploying Production ML Systems

Taking a machine learning model from an isolated notebook to a reliable production environment requires a completely different set of skills than just training algorithms. This course bridges the gap between data science and software engineering by introducing the core principles of Machine Learning Operations (MLOps). You will transition from writing manual ML scripts to designing automated, robust, and reproducible machine learning pipelines. Throughout this course, you will understand how to manage data, track experiments, package models, and monitor their performance in real-world scenarios. What you'll learn: - Understand foundational MLOps terminology, lifecycle stages, and the core differences between traditional DevOps and MLOps. - Configure automated data pipelines and version control systems for both code and datasets. - Track machine learning experiments, parameters, and model artifacts systematically. - Build automated CI/CD pipelines to test, package, and deploy models using containerization fundamentals. - Monitor production models for data drift, concept drift, and performance degradation. - Apply modern observability patterns to maintain system health and reliability. The course begins with essential definitions and lifecycle concepts before guiding you through data versioning, experiment tracking, deployment strategies, and continuous monitoring. You will learn through clear written explanations, architectural breakdowns, and practical text-based configuration exercises. Designed for aspiring ML engineers, data scientists, and software developers new to operations, this course requires only basic Python knowledge and no prior MLOps experience. Start building reliable, automated machine learning systems today.