Version Control for Machine Learning and MLOps Projects

Managing machine learning projects requires more than just tracking code; you must also manage massive datasets, model weights, and evolving training pipelines. This text-based course guides you through the essential principles of source control and MLOps to keep your machine learning workflows organized and reproducible. By reading through these structured lessons, you will transition from manual file tracking to professional version control workflows designed specifically for data science. You will understand how to manage code, handle large files safely, and collaborate effectively with other engineers. What you'll learn: Understand foundational source control concepts and how they apply to data science workflows; Track and version large datasets and model files using specialized tools like Git LFS and Data Version Control (DVC); Maintain complete reproducibility of your machine learning experiments and training pipelines; Apply collaborative branching strategies tailored for team-based data science research; Configure basic CI/CD concepts to automate model testing and validation. The course begins with core version control terminology and setup before moving into advanced data versioning strategies and pipeline automation. You will progress through clear, written explanations and code-based scenarios that simulate real-world team environments. This course is designed for beginner data scientists, machine learning enthusiasts, and software engineers looking to adopt MLOps practices, with no prior version control experience required. Start reading today to build a reliable, professional foundation for your machine learning projects.