Version Control Tools for MLOps focuses on managing changes to machine learning code, data, models, and configurations throughout the ML lifecycle. It enables organizations to improve collaboration, maintain reproducibility, and track the evolution of ML projects efficiently. This training explains core concepts such as source control, branching strategies, repository management, and change tracking. It also covers versioning for datasets, experiment tracking, model registries, and integration with CI/CD pipelines. You will learn how enterprises use tools like Git and ML-specific version control systems to manage machine learning workflows effectively. The course also highlights best practices for building organized, traceable, and production-ready MLOps environments.
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