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Ultimate LLMOps for LLM Engineering: Engineering Reliable, Observable, and Scalable LLM Systems (en Inglés)
Dand, Kinjal (Autor)
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Orange Education Pvt Ltd
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Ultimate LLMOps for LLM Engineering: Engineering Reliable, Observable, and Scalable LLM Systems (en Inglés) - Dand, Kinjal
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Reseña del libro "Ultimate LLMOps for LLM Engineering: Engineering Reliable, Observable, and Scalable LLM Systems (en Inglés)"
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 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 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 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
