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Core
Model Registry, Lineage, and Promotion
A model registry is the versioned source of truth for trained models: each model has a version, lineage (the data, code, config, and run that produced it), and a stage (staging, production, archived). It enables reproducibility, safe promotion through gates, instant rollback, and audit. Lineage is what lets you reproduce a model and debug a regression by diffing against the last good version. Applied-AI interviews probe it because shipping models without versioning and lineage makes rollback and debugging guesswork.
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RELATED CONCEPTS
PRACTICE THIS IN REAL QUESTIONS
MLOps & ML EngineeringHow do you make ML experiments reproducible and manage models from experiment to production?→MLOps & ML EngineeringA customer disputes a prediction your model made three months ago. How do you reproduce it exactly?→MLOps & ML EngineeringHow do you manage model versions and promote a model from staging to production safely?→MLOps & ML EngineeringHow do you put governance around models: approvals, access, model cards, and deprecation?→MLOps & ML EngineeringWhat exactly do you pin to make an ML training run bit-for-bit reproducible?→MLOps & ML EngineeringHow do you version large datasets in practice, and when do you reach for DVC versus lakeFS?→
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