Model and Data Governance for GenAI with Model Cards and AWS Glue
Discover how to design responsible generative AI systems on AWS by integrating model cards and AWS Glue Data Catalog. Learn to implement transparent, auditable, and compliant governance frameworks that ensure traceability of models and data assets, supporting secure and ethical AI deployments in regulated environments.
Governance is a defining requirement for production-grade generative AI systems, especially in regulated and enterprise environments. Unlike traditional applications, GenAI systems produce probabilistic outputs that depend on both the model’s behavior and the underlying data sources. For the AWS Certified Generative AI Developer – Professional (AIP-C01) exam, candidates are expected to design architectures that are transparent, auditable, and compliant.
This lesson explains how model cards and the AWS Glue Data Catalog work together to support those goals by documenting model behavior and establishing traceability across data assets. These mechanisms form a foundation for responsible AI practices on AWS.
Governance requirements for generative AI systems
Governance for generative AI focuses on transparency, accountability, and traceability. Stakeholders must be able to understand what a model is intended to do, where its inputs come from, and how its outputs are produced. This requirement becomes more ...