Here's the thing about large language they don't play by the old rules. Traditional MLOps completely falls apart when you're dealing with GenAI. The model hallucinates, security assumptions crumble, monitoring breaks, and agents can't operate. Suddenly you're in uncharted territory. That's exactly why LLMOps has emerged as its own discipline.
Managing Large Language Models in Production is your guide to actually running these systems when real users and real money are on the line. This book isn't about building cool demos. It's about keeping LLM systems running smoothly in the real world.
Navigate the new roles and processes that LLM operations require Monitor LLM performance when traditional metrics don't tell the whole story Set up evaluations, governance, and security audits that actually matter for GenAI Wrangle the operational mess of agents, RAG systems, and evolving prompts Scale infrastructure without burning through your compute budget
almost no practical examples, just a bunch of buzzwords compiled from articles and research papers, feels like a vibecoded book a good example he provides is from Andrej Karpathy youtube video "Lets build ChatGPT from scratch", but the example is useless, since you need to follow along with the video to wrap your head around it