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Ultimate LLMOps for LLM Engineering: Engineering Reliable, Observable, and Scalable LLM Systems
Paperback
ISBN13: 9789349887534
Publisher: Orange Education Pvt Ltd
Published: Feb 10 2026
Pages: 346
Weight: 1.31
Height: 0.72 Width: 7.50 Depth: 9.25
Language: English
From Prototype to Production-Grade LLM Systems.
Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● End-to-end coverage of modern LLMOps, from fundamentals to production deployment and monitoring.
● Hands-on prompt management, LLM chaining, RAG, and building AI agent examples.
Book Description
Large Language Models (LLMs) are transforming how organizations build intelligent applications, yet taking them from experimentation to reliable production systems requires a new discipline-LLMOps. Ultimate LLMOps for LLM EngineeringU offers a comprehensive journey through the principles, tools, and workflows essential for operationalizing LLMs with confidence and efficiency. It begins by demystifying LLM fundamentals, model behavior, and the evolving landscape of MLOps, giving readers the context needed to design scalable AI systems.
What you will learn
● Understand LLM foundations and how they integrate with the MLOps ecosystem.
● Build robust prompt strategies, LLM chains, and RAG pipelines for complex workflows.
● Design and deploy AI agents and autonomous LLM-driven systems.
● Serve, scale, monitor, and evaluate LLMs across cloud and on-prem environments.
Who is This Book For?
This book is tailored for GenAI Developers, Machine Learning Engineers, and Data Scientists who want to build, deploy, and manage LLM-powered systems at scale. Readers should have foundational knowledge of AI/ML concepts, basic NLP familiarity, and experience with Python programming to fully benefit from the content.
Table of Contents
1. Unveiling the World of Large Language Models
2. Getting Started with MLOps
3. Mastering Prompt Management for LLMs
4. The Power of LLM Chaining
5. Retrieval Augmentation Generation
6. AI Agents and Autonomous Systems
7. Deploying Large Language Models
8. Model Monitoring and Evaluation
9. LLM Fine-tuning and Adaptation
10. LLM Security, Privacy, and Drift Detection
11. LLMOps with Langfuse
12. Real-World Examples and Emerging Trends
Index
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