Generative AI Foundations on AWS

This unit covers technical foundations and practical development workflows for generative AI on AWS, including foundation models, LLMs, prompt engineering, and the business value and risks of adoption. Students will learn data preparation, model development, and MLOps practices using Amazon SageMaker, JumpStart, Canvas, and Amazon Bedrock, preparing them to design, prototype, and recommend deployment strategies in later units.

Learning Objectives

• Analyze foundation models and large language model (LLM) types by comparing architectures, capabilities, applications, and limitations
• Apply prompt engineering techniques to design, iterate, and quantify improvements in model outputs for targeted use cases
• Demonstrate end-to-end generative AI development workflows on AWS by performing data preparation, model development/selection, and MLOps tasks using Amazon SageMaker, JumpStart, Canvas, and Bedrock
• Evaluate enterprise value, operational risks, and ethical/legal considerations of generative AI solutions and recommend appropriate development and deployment strategies