This course introduces the fundamentals of ML system design combined with MLOps practices, helping beginners understand how to build complete machine learning systems that work in production. It focuses on the full lifecycle of machine learning, from system design and data flow to deployment and monitoring.

The course begins with an introduction to ML system design, explaining how machine learning applications are structured in real-world environments. You will learn how to think beyond models and focus on entire systems, including data pipelines, feature engineering, training workflows, and serving infrastructure.

A key part of the course is MLOps fundamentals, which show how machine learning operations ensure that models are reliable, reproducible, and scalable. You will learn how to manage experiments, track model versions, and automate workflows for continuous improvement.

The course also covers deployment concepts, including how models are served in production and how systems are monitored for performance and data drift. These skills are essential for maintaining real-world AI applications.

By the end of this course, you will understand how to design machine learning systems from an engineering perspective and apply MLOps principles to build scalable, production-ready AI solutions. This makes it ideal for beginners entering machine learning engineering and AI system design.