Every retraining run produces different artifacts.
Code changes, data changes, and hyperparameters change too.
Tracking what’s deployed is becoming confusing. Rollbacks are risky?
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Every retraining run produces different artifacts.
Code changes, data changes, and hyperparameters change too.
Tracking what’s deployed is becoming confusing. Rollbacks are risky?
Model versioning must include more than just the model file.
A reliable version should uniquely identify the training code, dataset snapshot, feature logic, and configuration. Hashes or version IDs tied to these components help ensure traceability.
Store model metadata alongside artifacts, including training time, data ranges, and metrics. This makes comparisons and rollbacks predictable.
Avoid versioning models based only on timestamps or manual naming conventions.
Common mistakes include:
Versioning only the
.pklor.ptfileLosing track of training data versions. Overwriting artifacts in shared storage
The practical takeaway is that a model version is a system snapshot, not just weights.