

AI Book Club: LLMOps
This is a casual-style event. Not a structured presentation on topics. Sometimes, the discussion even drifts away from the chapters, but feel free to grab the mic to help steer it back.
Feel free to join the discussion even if you have not read the book chapters! :)
Want to discuss the contents during the reading week? Join the Slack Flyte MLOps Slack group and search for the "ai-reading-club" channel. https://slack.flyte.org/
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About the book:
Title: LLMOps
Authors: Abi Aryan
Published: July 2025
https://learning.oreilly.com/library/view/llmops/9781098154196/
Chapters:
1. Introduction to Large Language Models
2. Introduction to LLMOps
3. LLM-Based Applications
4. Data Engineering for LLMs
5. Model Domain Adaptation for LLM-Based Applications
6. API-First LLM Deployment
7. Evaluation for LLMs
8. Governance: Monitoring, Privacy, and Security
9. Scaling: Hardware, Infrastructure, and Resource Management
10. The Future of LLMs and LLMOps
Book Description
Here's the thing about large language models: 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.
LLMOps: 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
https://learning.oreilly.com/library/view/llmops/9781098154196/