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This book is a comprehensive guide designed to equip engineers, data scientists, and AI practitioners with the principles, tools, and strategies needed to ensure reliability, performance, and accountability in Large Language Models (LLMs). The book begins by laying the groundwork with the foundations of observability, introducing LLMs, their significance in modern AI, and the critical role observability plays in maintaining robust systems. It then explores SRE principles, service level objectives, and incident response, while distinguishing the unique observability challenges that arise in AI and ML systems. Building on this foundation, the book dives into measuring performance, from defining SLOs tailored for LLMs to monitoring computational and token-level metrics. Readers gain practical insights into structured logging, debugging, and distributed tracing methods that provide visibility into complex LLM workflows. Scaling challenges are addressed through strategies for cross-model observability, autoscaling, latency reduction, and fault-tolerant infrastructure design. The book further explores chaos engineering, guiding readers through resilience testing in LLMs and the automation of chaos experiments in CI/CD pipelines. Finally, it highlights monitoring, retraining, and ethical considerations in AI observability, including governance, privacy, and accountability. In conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems. What you will learn:How to design observability pipelines for LLMs, including token-level logging, prompt tracing, and latency analysis. Techniques for applying chaos engineering principles to test LLM robustness under stress andfailure scenarios. Methods for building SLOs, SLAs, and dashboards tailored to inference quality and modelreliability. Strategies for monitoring hallucinations, drift, bias, and ethical failures in real-time. Who this book is for:This book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.
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I lager, skickas inom 4 till 5 vardagar
Bonaventura Di Bello
Janelle Shane
Craig Smith
Lee Vaughan
Tracy Fullerton
Alan Beaulieu
Alex Hidalgo
Mana Takahashi
Paul Kingsnorth
Nicola Askham
Martin Fowler
Alejandro Reyes
Justin Seitz, Tim Arnold
Jon Gjengset
Al Sweigart
Ruha Benjamin
Cole Nussbaumer Knaflic
Peter Yaworski
V. Anton Spraul
Susan Garrett
C G Jung
James G. Speight
David B. Weishampel, David E. Fastovsky
bell hooks
Sarah Easter Collins
Frida Skybäck
Liz Moore
Colleen Hoover
Kristin Fägerskjöld
Armando Lucas Correa
Sharon Bolton
Ruth Lillegraven
Santa Montefiore
Kelly Rimmer
Martha Hall Kelly
Svenja O'Donnell
Nathan Harris
Holly Jackson
Louise Boije af Gennäs
Tina Norin
Kristin Fägerskjöld
Pernilla Lindroos
Anna Lönnqvist
Jessie Burton
Malin Nordström
Marc Levy
Emma Donoghue
Snezana Lindskog, Andie Lindskog
Tony Samuelsson
Andrev Walden
Tina Harnesk
Kathrine Nedrejord
Anthony Doerr
Marieke Lucas Rijneveld
Lauren Groff
Claire Messud
Claire Lynch
Sebastian Barry
Ray Celestin
Simon Stranger
Alice Munro
Linda Boström Knausgård
Marit Kapla
Ulf Stark
Sarah Easter Collins
Liz Moore
Ursula Scavenius
Colleen Hoover
Michel Jean
