📱 eBook en inglés ULTIMATE LLMOPS FOR LLM ENGINEERING: ENGINEERING RELIABLE, OBSERVABLE, AND SCALABLE LLM SYSTEMS

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 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 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 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