Data Management and Compliance for AWS Generative AI

Deploying generative AI in production requires more than just calling an API; it demands robust data architecture and strict compliance safeguards. This text-based course guides you through the essentials of connecting foundation models to enterprise data sources securely on AWS. You will progress from foundational concepts of data privacy to implementing practical Retrieval-Augmented Generation (RAG) architectures. By reading through structured explanations and analyzing clear configuration examples, you will learn how to maintain data lineage, protect sensitive information, and manage high-performance vector stores. What you'll learn: 1. Understand the core principles of data governance and compliance for generative AI. 2. Configure secure vector databases and document stores within the AWS ecosystem. 3. Build robust Retrieval-Augmented Generation architectures to ground your models. 4. Implement zero-trust security patterns and data encryption for foundation models. 5. Apply modern MLOps practices to monitor data quality and compliance drift. 6. Practice designing privacy-preserving workflows for enterprise AI applications. The course starts with foundational definitions of model integration, vector embedding, and governance regulations, then moves step-by-step through architectural patterns, security configurations, and data management strategies. This course is designed for IT professionals, cloud enthusiasts, and developers who are new to AI data management and want to build compliant systems on AWS. No prior experience with generative AI engineering is required. Start reading today to master compliant data integration for AWS generative AI.