You're working on a shared ML pipeline where multiple engineers update training data, features, and model artifacts over time. You want a clean way to track what changed, reproduce past runs, and coordinate promotion to production without confusion.
What are the best practices for versioning datasets and models in a collaborative, production-grade MLOps environment?
