LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

This book was written by a pair of engineers who are well-known in their field, and it leaves nothing uncovered. The way they structure the content is something I haven’t seen in many online courses from prestigious educational institutions. It fully equips the reader to become a hero in whatever field they choose to apply this knowledge. The emphasis on MLOps and DEVOps ensures that what you build is scalable, highly professional, and measurable.

Probably full of useful, legitimate, and honest best practices to form the base for a LLM engineer reference. However, almost from the outset it is an overt commercial endeavor to sell pieces of the puzzle described in this book. But it doesn't end there. Nonetheless, although its' open source foundation, it is well written, it is illustrated to include copious code snippets, and it is from very credible sources.

I'm looking forward to second edition

I Learned how devops is implemented through LLM’s and essentially how to construct more modular code to bring LLM’s and ML models into production environments. I plan on integrating it fully on the cloud as proof of my learning!

this handbook is a great LLM starter guide

I've been working in software engineering for over 10 years and would like to know more about LLMs. This was a great resource to help me understanding LLMs from the ground up. I highly recommend this book to those who are in the same boat as me.

This book shows you exactly how to build and deploy LLM systems that work. Clear code, real AWS examples, and proper production setup steps. A must-buy for AI engineers who build actual products.

Great work, focusing not just on an LLM application development but also on the MLOps and CI/CD/CT of a service.