Model Registry and Release Pipelines
Version the inputs and reject a model that fails the release checks.
Background reading
A model file is only part of the release
Predictions depend on transformations, feature definitions, model parameters, thresholds, and runtime dependencies. Package or reference these together with immutable identities.
Register the evaluated artifact and promote that version. Retraining during deployment creates a different model whose evaluation result may not apply.
Promotion should be reversible
An experiment run records the data, parameters, metrics, and artifact it produced. A registry version identifies a candidate that another process can load. Neither a high score nor a registry entry is approval to serve users.
Compare the candidate with the current version on held-out data and important groups. Promote only after the checks pass, retain the previous version, and verify predictions after loading. An alias can move; record the resolved artifact version in each release and prediction log.
Further reading
Read the named section in Chip Huyen’s book for the ML lifecycle, then use the linked technical documentation for the lab. The 2022 book does not cover today’s LLM or agent runtime tooling.
Complete workflow
The new model saw tomorrow
Version the inputs and reject a model that fails the release checks.
Version the inputs and reject a model that fails the release checks.
The training table joins a status updated after the prediction timestamp. Remove the future information, rebuild time-correct features, and compare performance on a held-out period before promotion.
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