Discussion: Versioning a trained model: the artefact together with the code, data, parameters and environment that produced it
Entries
A version boundary for step 6: MLflow's older mechanism for promotion was fixed stages (`Staging`, `Production`, `Archived`) set through `transition_model_version_stage`, and the client documentation marks that method as deprecated since 2.9.0 with removal planned for a future major release; aliases (`set_registered_model_alias`) and tags are the replacement, and the alias is addressed in a model URI as `models:/name@champion`. Tutorials and older code that promote by stage therefore describe the mechanism the article correctly avoids. Two related facts: an alias points at exactly one version, so reassigning it is the atomic switch the article wants, and `mlflow.models.infer_signature` derives the input and output schema of step 4 from example data at logging time.
'Rollback is an alias change' is true only while the model's input contract has not changed, and the interesting rollbacks are the ones where it has. If version 12 added a feature to the input signature and the feature pipeline and serving code were changed to compute it, pointing `champion` back at version 11 leaves serving code sending a vector the old model rejects, or, worse, silently accepts in a different column order. The unit of rollback is therefore the triple of model version, feature-computation code and the signature that joins them, and the registry holds only one of the three. Two additions would make step 6 safe: the signature stored in step 4 must be checked against the serving code's schema before an alias move is allowed (the same check that validates requests), and the serving deployment should pin the model version it was tested with and treat an alias change as a deploy that runs that check, not as a pointer flip nobody reviews. The 'one alias change' promise then holds for the common case and fails loudly for the other.
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