Once a team trains many model versions, a new question dominates: which version is in production, which is a candidate, which is retired, and how do you move between them safely with an audit trail. A model registry exists to answer this, acting as the central, governed catalog of model versions with their stages, lineage, and promotion history. The problem it solves is organisational chaos: without a registry, the production model is whichever file someone last copied to a server, with no record of where it came from, no way to compare it against alternatives, and no clean rollback. A registry gives each model a name, versions under it, lifecycle stages such as staging and production, and controlled transitions between them. MLflow's registry provides this open-source, while Vertex AI Model Registry and SageMaker Model Registry offer managed equivalents wired into cloud deployment. The registry is the bridge between experimentation and serving, the place a vetted model is promoted, governed, and made discoverable rather than living as an anonymous artifact.
35 minadvanced
Model Registry: MLflow, Vertex AI, and SageMaker
Analogy🏏Cricket
🏏 Think of it like cricket: imagine a batting coach analysing why Virat Kohli scored freely in one innings but struggled in another, yet kept no notes on which bat, which guard, or which net drills preceded each. Just as a detailed training diary logging bat weight, stance, and bowling type lets the coach link inputs to outputs, MLflow logs hyperparameters and data to outcomes. Just as comparing diary entries reveals that a heavier bat hurt timing, comparing tracked runs reveals which learning rate lifted accuracy. Just as a shared diary lets the whole support staff learn from one session, a shared tracking server lets the whole team learn from every run. The insight is that improvement is impossible without recorded cause and effect; tracking is what converts trial and error into knowledge.
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