Reproducibility
The capacity to consistently regenerate AI model results using the same data, code, and configurations, ensuring transparency and auditability.
Definition
Requires strict version control of code, data, and environment (dependencies, hardware). Automated pipelines capture experiment metadata (random seeds, hyperparameters), register artifacts in model registries, and allow for exact reruns. Governance frameworks mandate reproducibility standards for all production models, with periodic audits of reproducibility and processes to remedy any divergences.
Real-World Example
A research lab uses MLflow to log every experiment’s dataset hash, code commit ID, Python environment, and random seed. Six months later, auditors successfully reran a critical experiment and reproduced published accuracy—demonstrating full traceability and reproducibility.
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