Version Control
The practice of managing and tracking changes to AI code, models, and datasets over time to ensure reproducibility and auditability.
Involves use of systems like Git for code, DVC or LakeFS for data, and model‐registry tools for artifact versions. Every change - feature-engineering scripts, hyperparameter settings, dataset snapshots, trained model binaries - is tagged and documented. Version control enables rollback, branch management for experimentation, and full traceability of how any production model was derived.
A financial‐services team stores its preprocessing code in Git, tracks raw and cleaned datasets via DVC, and registers each trained model in MLflow with its parameters and input data version. When anomalies appear, they can reproduce any previous model version exactly, aiding both debugging and audit compliance.
“What used to take weeks of manual reviews and policy work is now structured and auditable in Enzai within minutes. It’s the first time AI governance has felt operational, not theoretical.”
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Enzai provides enterprise-grade infrastructure to manage AI risk and compliance. It creates a centralized system of record where AI systems, models, datasets, and governance decisions are documented, assessed, and auditable.
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