How do you manage model versioning and experiment tracking in production ML systems? What tools and practices enable reproducibility?

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Model versioning and experiment tracking are essential for managing the ML lifecycle. Without systematic tracking, teams lose the ability to reproduce results, compare models, or roll back to previous versions when issues arise.