Generative AI Foundations and AWS Tools

This unit introduces foundational concepts of generative AI—including terminology, foundation models, LLMs, applications, and ethical/operational challenges—while bridging theory to practice through data preparation, model development, and MLOps using AWS services. Students will learn prompt engineering and evaluate AWS tools (Amazon SageMaker, JumpStart, Canvas, Bedrock) to design, develop, and operationalize generative AI solutions for enterprise value.

Learning Objectives

• Analyze foundational generative AI concepts and terminology (foundation models, LLMs, model types, applications, and common challenges) to explain their roles in AI system design
• Apply prompt engineering techniques and data preparation best practices to prepare datasets and craft prompts that improve generative model outputs
• Demonstrate model development and MLOps workflows using AWS tools (Amazon SageMaker, SageMaker JumpStart, SageMaker Canvas, Amazon Bedrock) by implementing a basic training-to-deployment pipeline
• Evaluate business value, risks, and strategy options to select an appropriate AWS-centered approach for developing and scaling a generative AI application within an enterprise