Generative AI Governance with AWS Glue and Model Cards

As generative AI moves from experimental labs to production systems, maintaining data integrity, compliance, and model transparency has become a business-critical requirement. Ensuring your training pipelines and model deployments are fully auditable is no longer optional. This text-based course guides you through establishing robust model and data governance frameworks on the AWS cloud platform. You will start with the core definitions of AI governance, understanding regulatory expectations, and exploring how to document model behavior systematically. Next, you will learn to structure data catalogs and maintain metadata lineage using AWS Glue, ensuring your generative workflows remain transparent and reproducible. What you'll learn: - Understand the core principles of AI governance, compliance requirements, and data lineage - Document model behavior, limitations, and ethical considerations using Model Cards - Catalog machine learning datasets and manage metadata using AWS Glue Data Catalog - Implement data quality checks and schema drift detection for generative AI pipelines - Apply zero-trust data access policies and security best practices for model artifacts - Design audit-ready workflows that trace data from raw ingestion to model deployment This course begins with foundational concepts, defining key terminology in AI governance and data stewardship before moving into practical implementation strategies. You will study structured text explanations, architectural patterns, and configuration examples that show you how to connect AWS Glue with model documentation workflows. This course is designed for data professionals, cloud practitioners, and aspiring AI engineers who want to build responsible, compliant AI systems. No prior experience with governance frameworks is required, though a basic familiarity with cloud concepts is helpful. Start reading today to build secure, transparent, and compliant generative AI workflows.