This unit introduces foundational AI and ML concepts, generative AI workflows (including RAG and agents), and an overview of AWS managed AI/ML services. Learners will explore model selection, basic prompt engineering, MLOps phases, and responsible AI principles to prepare for applied development and deployment in subsequent units.
Learning Objectives
- Analyze the historical development, key trends, and real-world applications of AI and ML to explain their impacts on industries and society
- Demonstrate knowledge of basic AI and generative AI concepts and terminology by describing foundation model types, RAG workflows, and agent-based interactions
- Apply decision criteria to select appropriate AWS managed AI/ML services and foundation models for specified use cases, including basic prompt engineering considerations
- Evaluate responsible AI principles, security/privacy risks, and MLOps phases to recommend governance, risk mitigation, and deployment practices for AI solutions
Curriculum
- 1 Section
- 14 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 1 — AI Foundations and AWS Services14
- 1.1A Brief History of AI20 mins
- 1.2A New Way to Interact with AI20 mins
- 1.3Key Trends in AI and ML Applications20 mins
- 1.4An Overview of AWS Managed AI and ML Services20 mins
- 1.5Selecting the Right Foundation Model for Your Use Case20 mins
- 1.6Retrieval-Augmented Generation Workflow20 mins
- 1.7MLOps Phases20 mins
- 1.8Key Principles of Responsible AI20 mins
- 1.9Basic AI Concepts and Terminology20 mins
- 1.10Basic Concepts of Generative AI20 mins
- 1.11Applications of AI and ML in Real-World Use Cases20 mins
- 1.12AWS AI and ML Services20 mins
- 1.13Model Selection and Prompt Engineering20 mins
- 1.14Generative AI Applications with RAG and Agents20 mins
Instructor
Free
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