9. Governance

ML governance encompasses a set of processes and frameworks that help in the deployment of ML models. It includes model explainability, auditability, traceability, and other more abstract but essential requirements of a successful end-to-end ML lifecycle.

9.1 Data quality and compliance	The ML system accounts for personal identifying information (PII) considerations, including anonymization. It has documented and reviewed column-level lineage for understanding the source, quality, and appropriateness of the data. It also has automated data quality checks for anomalies.
9.2 Audit and documentation	The ML system has a full log of all changes during development, including experiments run and reasons for choices made for regulatory compliance.
9.3 Reproducibility and traceability	The ML system includes a full data snapshot for precise and rapid model re-instantiation, or it has the ability to recreate the environment and retrain with a data sample.
9.4 Human-in-the-loop signoff	The ML system has manual verification and authorization for regulatory compliance. The system requires signoffs for every environment move (for example, Dev, QA, pre-Prod, and Prod).
9.5 Bias and adversarial attacks testing	The ML system has Red Team adversarial testing using multiple tools and attack vectors, and automated bias checking on specific subpopulations. This component ties back to the Observability and model management section.