Designing Generative AI Architectures with Bedrock on AWS

Building generative AI applications requires more than just calling an API; it demands a secure, scalable, and well-designed architecture. This text-based course guides you through utilizing Bedrock on AWS to design robust AI solutions from the ground up. You will transition from understanding basic generative AI concepts to designing production-ready cloud architectures. You will learn how to evaluate foundation models, implement secure retrieval-augmented generation (RAG) patterns, and deploy AI agents that safely interact with your existing data sources. What you'll learn: Understand core generative AI terminology, foundation model types, and the Bedrock ecosystem; Evaluate and select the right foundation models for specific business use cases; Configure retrieval-augmented generation (RAG) architectures to ground models in private data; Implement security best practices, including data privacy controls and guardrails; Design agentic workflows that orchestrate multi-step tasks autonomously; Apply cost-optimization and monitoring strategies for model deployment. The course begins with foundational definitions and key AI concepts before moving into practical architectural patterns. Through clear written explanations and structured design exercises, you will master the principles of building secure AI systems on AWS. This course is designed for cloud architects, developers, and technology enthusiasts who are new to generative AI on AWS. No prior machine learning or AI experience is required, though a basic familiarity with cloud concepts is helpful. Start reading today to build your foundation in generative AI architecture.