Definition
Models with the same parameters and identical inputs may generate different outputs, causing challenges to reproduce a specific output and determine accuracy across output variations.
Controls & guardrails that address this
4Grouped by control function, with the AI lifecycle stage(s) to apply each and the other risks it addresses. Filter by control category below.
Implement model versioning and experiment tracking as a governance requirement during build. Gate model promotion on version registry entry.
Document all regularisation parameters and normalisation configurations in the model card. Store version-controlled.
Maintain version-controlled records of each fine-tuning run including dataset version, hyperparameters, and random seeds.
Periodically validate that deployed model versions remain reproducible. Test rollback procedures annually or after major updates.