{"product_id":"9798868828263","title":"Observability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale","description":"\u003ch1\u003eObservability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale\u003c\/h1\u003e \u003ch2\u003eSharma, Ankush\u003c\/h2\u003e \u003cp\u003e\u003c\/p\u003e\u003cp\u003eThis 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). \u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eIn conclusion, this book provides a holistic roadmap to building reliable, transparent, and future-ready LLM systems.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWhat you will learn:\u003c\/p\u003e\u003cul style=\"list-style-type: disc\"\u003e\n\u003cli\u003eHow to design observability pipelines for LLMs, including token-level logging, prompt tracing, and \u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003elatency analysis.\u003c\/p\u003e\u003cul style=\"list-style-type: disc\"\u003e\n\u003cli\u003eTechniques for applying chaos engineering principles to test LLM robustness under stress and\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003efailure scenarios.\u003c\/p\u003e\u003cul style=\"list-style-type: disc\"\u003e\n\u003cli\u003eMethods for building SLOs, SLAs, and dashboards tailored to inference quality and model\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003ereliability.\u003c\/p\u003e\u003cul style=\"list-style-type: disc\"\u003e\n\u003cli\u003eStrategies for monitoring hallucinations, drift, bias, and ethical failures in real-time.\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003cp\u003e\u003c\/p\u003e\u003cp\u003eWho this book is for:\u003c\/p\u003e\u003cp\u003eThis book is for AI infrastructure engineers, SREs, machine learning platform teams, and applied AI practitioners deploying or maintaining LLM-based applications.\u003c\/p\u003e \u003ch3\u003eDetails\u003c\/h3\u003e \u003cp\u003ePublished by: Apress\u003c\/p\u003e \u003cp\u003ePublication Date: 2026-07-15\u003c\/p\u003e \u003cp\u003eFormat: Paperback\u003c\/p\u003e \u003cp\u003eISBN-13: 9798868828263\u003c\/p\u003e \u003cp\u003eDOI: \u003c\/p\u003e \u003cp\u003eDimensions: 254cm x178cm\u003c\/p\u003e \u003cp\u003ePages: \u003c\/p\u003e ","brand":"Apress","offers":[{"title":"Default Title","offer_id":47560144879756,"sku":"9798868828263","price":49.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0710\/9545\/1788\/files\/9798868828263.jpg?v=1778010596","url":"https:\/\/fh90cf-fv.myshopify.com\/products\/9798868828263","provider":"Late Knight Books and Services, LLC","version":"1.0","type":"link"}