Observability for Large Language Models : Site Reliability and Chaos Engineering for AI at Scale

Language: English

Published by Apress Okt 2026, 2026

9798868828263

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  • New
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Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH

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Softcover

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Neuware - 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.…

Seller Inventory # 9798868828263

Title
Observability for Large Language Models : Site Reliability and Chaos Engineering for AI at Scale
Author
Ankush Sharma
Publisher
Apress Okt 2026
Publication year
2026
Condition
Neu
Binding
Taschenbuch
Language
English
ISBN 13
9798868828263
Item weight
503 grams
Dimensions
254x178x15 mm

AHA-BUCH GmbH

Einbeck, Germany

5-star seller

AbeBooks seller since August 14, 2006

Shipping rates from Germany to U.S.A.

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Einbeck, Germany 37574