Implementing governance across the AML lifecycle
Ensuring governance during data ingestion, preprocessing, and model training involves data quality management, version control, and ethical considerations. IT governance during model evaluation and validation includes rigorous testing protocols, bias detection, and performance monitoring to ensure compliance and model integrity.
Governance in model deployment and monitoring involves continuous monitoring for performance drifts, ensuring that models remain reliable and compliant with regulations. There will be more details in the next chapter.
AML provides a range of tools and features to support governance across the ML lifecycle, including experiment tracking, model registry, pipelines, and audit logs.
After establishing governance across the AML lifecycle, securing the data and lineage information becomes paramount. Effective governance isn’t just about tracking and documenting processes but also about ensuring...
