How do you document and manage the lifecycle of an ML model in production?

Instruction: Discuss the practices and tools you use for documenting and managing ML models throughout their production lifecycle.

Context: This question assesses the candidate's approach to documentation and lifecycle management of ML models in a production environment.

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The way I'd approach it in an interview is this: I document the model lifecycle from problem framing through retirement: data sources, feature logic, training runs, validation results, deployment history, owner, intended use, limitations, monitoring setup, and rollback or...

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How do you document and manage the lifecycle of an ML model in production?

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