How do you manage dependencies in an ML project to ensure consistency across development and production environments?

Instruction: Discuss the methods you employ to handle and document dependencies in machine learning projects to maintain environment consistency.

Context: This question evaluates the candidate's practices for dependency management, a key aspect of MLOps that supports the smooth transition of models from development to production without discrepancies.

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The way I'd approach it in an interview is this: I lock dependencies explicitly, build reproducible environments, and keep training and serving environments defined as code instead of relying on ad hoc manual setup. That usually means pinned...

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