Summary

Abi Aryan, founder of Abide AI and a machine learning engineer with close to a decade building production ML systems, wrote this for teams who got a prototype working and are now facing what happens once real users depend on it. Its starting claim is that traditional MLOps doesn't transfer: models hallucinate instead of simply being wrong, prompt injection is a new attack surface, and agents introduce failure modes that standard monitoring doesn't catch. From there it works through LLMOps team roles, the architecture of RAG and agent-based applications, and the data engineering underneath them, before turning to evaluation metrics, early detection of hallucination and other critical failures, governance and privacy, and building an observability pipeline that scales without the infrastructure bill scaling with it.

Target Readers

• ML engineers and SREs handed an LLM prototype and told to make it production-ready, with no playbook for what breaks differently from a traditional model
• Teams building RAG or agent-based applications who need an evaluation strategy for hallucination and failure, not just a demo that worked once
• Engineering leads defining governance and security review for LLM systems, including prompt injection, ahead of a production launch

Colophon

Publisher

オライリー・ジャパン

ISBN

978-4-8144-0160-4

Published

May 2026

List price

¥5,280incl. taxMay differ from the actual selling price on Amazon