Ultimate LLMOps for LLM Engineering - Engineering Reliable Observable and Scalable LLM Systems
Kinjal Dand
Editora: Orange Education Pvt Ltd
Sinopse
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.● Practical insights into LLMOps observability and analytics using LangFuse, Fine-tuning, and securing LLMs in real-world environments.Book DescriptionLarge 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 Engineering 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.The core chapters dive into hands-on techniques that drive real-world LLM applications, including prompt management, LLM chaining, and Retrieval Augmented Generation (RAG). You will explore how to design LLM pipelines, build effective agentic systems, and orchestrate complex multi-step reasoning workflows. Each concept is supported with practical insights applicable across industries and platforms.Moving deeper into production, the book equips you with strategies for deploying, serving, and monitoring LLMs in modern cloud and hybrid environments. You will learn how to fine-tune and adapt models, enforce security and privacy requirements, and detect model drift in dynamic data ecosystems.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.● Apply fine-tuning, optimization, and adaptation techniques to improve model performance.● Implement best practices for LLM security, privacy, governance, and drift detection.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 Contents1. Unveiling the World of Large Language Models2. Getting Started with MLOps3. Mastering Prompt Management for LLMs4. The Power of LLM Chaining5. Retrieval Augmentation Generation6. AI Agents and Autonomous Systems7. Deploying Large Language Models8. Model Monitoring and Evaluation9. LLM Fine-tuning and Adaptation10. LLM Security, Privacy, and Drift Detection11. LLMOps with Langfuse12. Real-World Examples and Emerging Trends Index
Disponível desde: 13/02/2026.
Comprimento de impressão: 344 páginas.
