Foundations and AWS GenAI Tools

This unit introduces foundational concepts and practical AWS tools for building generative AI solutions, covering foundation model types (including LLMs), prompt engineering, and the business value of GenAI. Students will learn data preparation, model development, and MLOps workflows using Amazon SageMaker, SageMaker JumpStart, SageMaker Canvas, and Amazon Bedrock while considering ethical, privacy, and deployment challenges.

Learning Objectives

• Analyze and compare foundation model types (e.g., LLMs, vision, multimodal) and their appropriate applications, strengths, and limitations
• Apply prompt engineering techniques to design, iterate, and empirically evaluate prompts that optimize model outputs for targeted tasks
• Demonstrate data preparation and model development workflows on AWS by preparing datasets, selecting training approaches, and using Amazon SageMaker, JumpStart, Canvas, or Bedrock to prototype models
• Evaluate enterprise deployment strategies and MLOps options—considering cost, scalability, data privacy, bias mitigation, and governance—to recommend an appropriate development and deployment plan