Observability for Large Language Models

Ankush Sharma

ISBN 13: 9798868828263

Editore: Apress Okt 2026, 2026

Riguardo questo articolo

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.

Codice articolo 9798868828263

Dati bibliografici

• Titolo: Observability for Large Language Models
• Editore: Apress Okt 2026
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798868828263
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 254 L x 178 A x 15 P (millimetri) 254 millimetri di larghezza per 178 millimetri di altezza per 15 millimetri di profondità
• Peso: 503 grammi

Descrizione articolo

Riassunto

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 and

failure scenarios.

• Methods for building SLOs, SLAs, and dashboards tailored to inference quality and model

reliability.

• 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.

Informazioni sull'autore

Ankush Sharma is a veteran technologist and AI systems architect with over 20 years of expertise in distributed systems, cloud infrastructure, and AI platform engineering. He has led engineering teams at leading global technology companies and has been an active contributor to open-source AI infrastructure projects. His work has been recognised through conference talks, patents, and leading developer forums. He is based in the Bay Area, US.