I work at the intersection of AI, cloud, and DevOps, and the AI side of that kept producing notes worth keeping in one place - which AWS service to reach for, the concepts behind the choice, and what changes once you move from reading about Bedrock to building with it. This series is where those notes live.
- Chapter 1 (Fundamentals) - concept-focused, assumes no prior AI/ML background.
- Chapters 2-3 (build) - assume Chapter 1 as background, hands-on with specific AWS services.
- Chapters land as the hands-on work behind them gets done, not on a fixed schedule.
The series
Chapter 1 - Fundamentals
Algorithm types, performance metrics, AWS AI services, inference options, Bedrock vs SageMaker, and prompt engineering techniques - the concepts I kept returning to.
Chapter 2 - Building with Bedrock Knowledge Bases (coming soon)
Hands-on walkthrough of building a RAG application with Bedrock Knowledge Bases - what works, what surprised me, and when RAG is the right call over fine-tuning.
Chapter 3 - Bedrock vs SageMaker: Choosing the Right Tool (coming soon)
A practical decision framework for Bedrock vs SageMaker, drawn from building with both rather than comparing feature lists.
Quick reference - which AWS AI/ML service for which job:
| Need | Reach for |
|---|---|
| Use a foundation model via API, no infrastructure to manage | Bedrock |
| Ground a foundation model’s answers in your own documents | Bedrock Knowledge Bases |
| Train or fine-tune a custom model on your own data | SageMaker |
| Pre-built AI for a specific task (OCR, translation, transcription) | Textract, Translate, Transcribe, Comprehend, Rekognition |
Notes
- This series pairs with my AIF-C01 study notes - the fundamentals chapter is the concept reference behind that cert.
- AWS AI services move fast, particularly Bedrock - treat service-specific chapters as a snapshot of what was true when written, not a permanent reference.