This unit introduces foundational AI and machine learning concepts, a brief history, and current trends while surveying AWS-managed AI and ML services. Students will learn model selection, retrieval-augmented generation (RAG) workflows, basic prompt engineering, MLOps phases, and principles of responsible AI to prepare for hands-on development and deployment in later units.
Learning Objectives
- Analyze the historical development of artificial intelligence and machine learning and summarize key trends and real-world application domains.
- Demonstrate understanding of basic AI and generative AI concepts and terminology and describe the purpose and use-case fit of core AWS-managed AI and ML services.
- Apply model-selection criteria, basic prompt-engineering techniques, and design a retrieval-augmented generation (RAG) or agent-based workflow for a defined use case.
- Evaluate the phases of the MLOps lifecycle and key principles of responsible AI to propose governance, monitoring, and ethical risk-mitigation strategies for deploying AI solutions.
Curriculum
- 1 Section
- 14 Lessons
- 30 hours
Expand all sectionsCollapse all sections
- Unit 1 — Foundations of AI on AWS14
- 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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