Your question is Versioning Datasets and Models. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
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?