Episode 41 — Control Training and Tuning: Reproducibility, Versioning, and Provenance Discipline (Domain 3)
Effective risk management during the training and fine-tuning phases requires rigorous discipline to ensure that AI models are both predictable and auditable. This episode focuses on the necessity of reproducibility, where a model can be recreated exactly using the same data, code, and hyperparameters. For the AAIR exam, candidates must understand the role of versioning—not just for the model code, but for the specific training datasets and environment configurations used. We explore the concept of provenance discipline, which involves maintaining a clear record of the origin and transformations of every component that influences the final model output. Best practices include the use of automated pipelines that log every tuning iteration to prevent "experimentation drift" where a high-performing model is deployed without a clear understanding of its internal logic. By maintaining this level of technical transparency, organizations can troubleshoot performance regressions and provide auditors with clear evidence of how a model was constructed and why it behaves as it does. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
