| 1.1 · AI terminology and learning approaches |
01 AI, Machine Learning and Service Selection |
| 1.2 · Use cases and service selection |
01 AI, Machine Learning and Service Selection, 02 The ML Lifecycle and MLOps |
| 1.3 · The ML lifecycle and its metrics |
02 The ML Lifecycle and MLOps |
| 2.1 · Generative and agentic AI concepts |
03 Foundation Models and Generative AI, 05 Agents, Tools and AWS AI Platforms |
| 2.2 · Business value and model limitations |
03 Foundation Models and Generative AI, 06 Evaluation and Responsible AI |
| 2.3 · AWS AI platforms and cost trade-offs |
03 Foundation Models and Generative AI, 05 Agents, Tools and AWS AI Platforms |
| 3.1 · Foundation-model application design |
03 Foundation Models and Generative AI, 04 Prompt Engineering, RAG and Fine-Tuning, 05 Agents, Tools and AWS AI Platforms |
| 3.2 · Prompting and prompt management |
04 Prompt Engineering, RAG and Fine-Tuning |
| 3.3 · Model training and adaptation |
02 The ML Lifecycle and MLOps, 04 Prompt Engineering, RAG and Fine-Tuning |
| 3.4 · Evaluation methods |
06 Evaluation and Responsible AI |
| 4.1 · Responsible AI practices |
06 Evaluation and Responsible AI |
| 4.2 · Transparency and explanations |
06 Evaluation and Responsible AI |
| 5.1 · Securing AI applications |
07 AI Security, Privacy and Governance |
| 5.2 · Governance and compliance |
07 AI Security, Privacy and Governance |
