This MLOps Zoomcamp course provides a structured introduction to building machine learning systems in a production-oriented way. It focuses on practical workflows used by real ML engineers, including environment setup, data handling, experiment tracking, and model lifecycle management.
The course begins with an introduction to MLOps concepts and explains the overall course structure and goals. You will learn how to configure a development environment using GitHub Codespaces and prepare your system for machine learning experimentation.
It also covers data processing fundamentals, including working with Parquet files instead of CSV, which is a more efficient format for large-scale ML workflows. An optional project demonstrates training a ride duration prediction model to apply these concepts in practice.
As the course progresses, it introduces the MLOps maturity model, helping you understand how organizations evolve from basic ML workflows to fully automated production systems.
A major part of the course focuses on MLflow, where you will learn experiment tracking, model logging, and model management. It then advances into model registry concepts, showing how models are versioned, stored, and prepared for deployment.
By the end of this Zoomcamp module, you will have a strong understanding of MLflow-based MLOps workflows, experiment tracking systems, and model lifecycle management in real production environments.
