Knowledge MaxxingModel registry
A model registry is a system that stores trained models along with their metadata, such as version, training data, and performance, so specific models can be found and deployed reliably.
Prerequisite: Data versioning
What matters
Without a registry, teams risk losing track of which trained model file corresponds to which experiment, making it hard to know exactly what is running in production.
Example
A model registry might store 'fraud-model v12', linking it to the exact training data version, hyperparameters, and evaluation scores that produced it.
Common misconception
A model registry is not just file storage; it specifically links each stored model to the metadata needed to know what it is and where it came from.
Recall question
What problem does a model registry solve beyond simply storing model files?