Model and Data Governance for GenAI with Model Cards and AWS Glue
Explore how to implement robust governance for Generative AI on AWS using model cards and AWS Glue Data Catalog. Understand how these tools document model behavior and track data lineage, ensuring transparency and compliance. This lesson helps you design auditable, responsible AI architectures that meet regulatory requirements and support exam scenarios focused on governance.
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 ...