Book review - AI Engineering by Chip Nyugen

Debate on why AI Engineering How AI engineering is different from traditional ML engineering / Ops

Basics of transformers and LLMs

1. why i wanted to read this book?
2. humble take on it variety of topics one might find informative if you are new in ml would be a great place for swe to gather ai engineering topics to further build upon surface scratching of the topics

chap 1. building AI powered apps - why the author choose AI engineering and not ML ml eng data - model - product ai eng product - data - model

recent growth in ml models and their genrative usage

chap2. understanding foundational models: author desrcibes how models are build - data gathering - modeling ie model architectures - post training + sampling + enforcing output patterns probabilistic nature of the models - needs determinsm when relaibility required

chap3 and chap 4. evaluation of model and ai systems model - parama entropy, cross entropy, perplexity, llm as judge - systems - deissgn evaluation pipeline, criteria, model selction, chapter 5: prompt engineering

chap6: Rag and agents

chap7: fine tuning

chap8: data data values: quality, quantity, coverage synthetic data and usage

chap9: inference optimization

chap10: ai engineering architecture and user feedback