Observability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale - Image 1

£39.28

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

Name: Observability for Large Language Models: Site Reliability and Chaos Engineering for AI at Scale
Brand: Apress
Price: 39.28 GBP
Availability: InStock

Higher Education

New to our records — first sighting 18 days ago. We'll learn its rhythm.

18 days of data · current price £39

NEW HERE?

Amazon shows you one price. We show you all of them.

Tosheroon watches Amazon prices so you don't have to. Every product on Amazon has a price history — we make it visible. Set the price you'd actually pay, and we'll email you the second it gets there. No app, no account, one email.

WHAT'S ON THIS PAGE

↓ Price chart
when this has been cheap or pricey

↓ Forecast
where the price is heading next

↓ Statistics
all-time high & low, recent range

↑ Price alert
name your number, we'll email you

Price History & Forecast

Grey patches = out of stock. Cheaper = lower on the chart. Hover for exact prices.

Last 13 days · 13 data points (no recent data)

Historical

Generating forecast…

Price Distribution

Price distribution over 13 days • 1 price levels

Days at Price

Price Analysis

Most common price: £39 (13 days, 100.0%)

Price range: £39 - £39

Price levels: 1 different prices over 13 days

Description

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.

Product Specifications

Brand: Apress
Format: paperback
ASIN: B0GW1X7ZRB
Category: Books > Subjects > Science, Nature & Maths > Engineering & Technology > Education > Higher Education
Domain: Amazon UK
Release Date: 26 June 2026
Listed Since: 04 April 2026

Barcode

No barcode data available

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

New to our records — first sighting 18 days ago. We'll learn its rhythm.

Price History & Forecast

Price Distribution

Price Analysis

Description

Product Specifications

Barcode

Similar Products You Might Like

Intermediate Python and Large Language Models

Large Language Models - The Hard Part: Open Source AI Solutions for Common Pitfalls

Building Generative AI-Powered Apps: A Hands-on Guide for Developers

Agentic AI Unleashed: A guide to designing, building, and deploying autonomous AI systems (English Edition)

LLMs in Practice: Real World Applications, Challenges and Success Stories

A Practical Guide to Generative AI Using Amazon Bedrock: Building, Deploying, and Securing Generative AI Applications

Generative AI with LangChain: Build large language model (LLM) apps with Python, ChatGPT, and other LLMs

Agentic Reliability Engineering: Building Agentic Systems That Think, Adapt, and Recover

Fundamentals of Data Observability: Implement Trustworthy End-To-End Data Solutions

Building Enterprise-Ready Azure Infrastructure: Learn Security, Scalability, and Reliability with IaC & DevOps

A Common-Sense Guide to AI Engineering: Build Production-Ready LLM Applications

Organizing for Generative AI and the Productivity Revolution: Reshaping Organizational Roles in the Age of Artificial Intelligence

Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms

Artificial Intelligence and Large Language Models: An Introduction to the Technological Future

Machine Learning Engineering with Python: Manage the production life cycle of machine learning models using MLOps with practical examples

Data Science First: Using Language Models in AI-Enabled Applications

LLMs in Action: Text, Tables, Images and Sound

Cracking AI Engineering Interviews: A Practical Field Guide to Coding, Systems, ML, and Engineering Judgment

Model Context Protocol for LLMs: Build secure, scalable, and context-aware AI agents using a standardized protocol

Data Engineering with Generative and Agentic AI on AWS: Building an AI-Augmented Data Practice for the Enterprise

Identity Analytics: Analytics for Identity and Access Management

Knowledge Graphs and Large Language Models: Current Approaches, Challenges, and Future Directions (Intelligent Data-Centric Systems)

The Deep Learning Architect's Handbook: Build and deploy production-ready DL solutions leveraging the latest Python techniques

Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python