Large Language Models (LLMs) Handbook : A Comprehensive Guide to Building, Fine-Tuning, Deploying, and Optimizing LLMs in Production

Sammy Tech

ISBN 13: 9798255541904

Editore: Roxanne Hamil Apr 2026, 2026

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Codice articolo 9798255541904

Dati bibliografici

• Titolo: Large Language Models (LLMs) Handbook : A Comprehensive Guide to Building, Fine-Tuning, Deploying, and Optimizing LLMs in Production
• Editore: Roxanne Hamil Apr 2026
• Data di pubblicazione: 2026
• Lingua: Inglese
• ISBN 13: 9798255541904
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 229 L x 152 A x 10 P (millimetri) 229 millimetri di larghezza per 152 millimetri di altezza per 10 millimetri di profondità
• Peso: 258 grammi

Descrizione articolo

Riassunto

Large Language Models have transformed the field of artificial intelligence. What began as research prototypes has evolved into powerful production systems driving real business value across industries. Yet moving from impressive demos to reliable, scalable, and cost-effective deployments remains one of the biggest challenges facing AI teams today.

This handbook bridges that critical gap.

Written for experienced developers, AI engineers, and technical leaders, Large Language Models (LLMs) Handbook delivers a complete, end-to-end guide to the modern LLM lifecycle. From transformer fundamentals and pre-training principles to advanced fine-tuning, preference alignment, retrieval-augmented generation (RAG), agentic systems, and production-grade inference optimization — every stage is covered with clarity, depth, and practical focus.

You will learn how to:

• Build and train transformer-based models from scratch
• Master parameter-efficient fine-tuning with LoRA, QLoRA, and DoRA
• Align models using RLHF, DPO, ORPO, and other state-of-the-art techniques
• Design robust RAG systems — from naive retrieval to Graph RAG and Agentic RAG
• Build autonomous agents with ReAct, tool use, memory, planning, and multi-agent collaboration
• Optimize inference with quantization (GPTQ, AWQ, GGUF), speculative decoding, and continuous batching
• Deploy and serve models efficiently using vLLM, TensorRT-LLM, and Ollama
• Establish production-grade LLMOps with monitoring, observability, versioning, and governance
• Navigate security, ethics, cost optimization, and enterprise scaling challenges

Each chapter combines rigorous technical explanations with complete, production-ready code examples, architectural diagrams, real-world production tips, hands-on exercises, and checklists you can apply immediately.

Whether you are fine-tuning open-source models, building sophisticated agentic workflows, or scaling LLM applications to thousands of users, this handbook provides the practical knowledge and battle-tested patterns needed to succeed in 2026 and beyond.

Perfect for:

• Machine Learning Engineers and AI Practitioners
• Software Engineers moving into AI systems
• Technical leaders and architects responsible for LLM initiatives
• Anyone building production-grade applications with Large Language Models

Stay ahead in one of the fastest-moving fields in technology. Master the full LLM lifecycle — from research to reliable production systems.