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SKU/Artículo: AMZ-B0GFDBYCTT

Building LLMs from Scratch: Designing, Training, Evaluating, and Deploying a Large Language Models: Case Study of Building LLMs: Transformer, Data Pipelines, Training, RAG, and Production Deployment

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Paperback

Audiobook

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Paperback

Detalles del producto

Disponibilidad:
En stock

Peso con empaque:
0.70 kg

Devolución:
Sí

Condición
Nuevo

Producto de:
Amazon

Viaja desde
USA

Sobre este producto

Building LLMs from Scratch is a complete, end-to-end guide to designing, training, evaluating, and deploying a real Large Language Model—not a toy example, not a wrapper around an API, and not a collection of disconnected tutorials.This book walks you through a single, cohesive use case: building a production-ready Engineering Copilot LLM from the ground up. Every chapter builds on the previous one, showing how modern LLM systems are actually constructed, governed, optimized, and maintained in the real world.You will learn not just how LLMs work, but how to engineer them responsibly. What This Book CoversThis book takes you through the entire LLM lifecycle, including:Designing a transformer-based language model from first principlesBuilding and training a custom tokenizer for technical contentPretraining and fine-tuning for structured, disciplined outputsImplementing Retrieval-Augmented Generation (RAG) with authoritative sourcesIntegrating deterministic tools to eliminate numeric hallucinationsEnforcing strict schemas, safety rules, and refusal behaviorDesigning ethics, liability boundaries, and audit logging requirementsOptimizing inference for performance, cost, and scalabilityDeploying the LLM as a production service with clear API contractsManaging versions, updates, regression testing, and long-term maintenanceEvery concept is backed by real file structures, real code, real configuration artifacts, and clear explanations of why each component exists. What Makes This Book DifferentMost LLM books focus on prompts, APIs, or theory. This book focuses on systems engineering.You will not just learn:what transformers are, but how to build onewhat RAG is, but how to govern and audit itwhat safety means, but how to enforce it in codewhat deployment looks like, but how to keep it stable over timeBy the end of the book, you will understand how to build an LLM that is:grounded in real datanumerically trustworthyrefusal-aware and ethically boundedauditable and defensibledeployable in real production environments Who This Book Is ForThis book is ideal for:Software engineers and ML engineers who want to truly understand LLM systemsTechnical professionals building AI tools for regulated or high-risk domainsEngineers who want more than API usage—they want ownership and controlArchitects and technical leads designing AI-powered systemsAdvanced learners who want to move from “using AI” to engineering AINo prior deep learning research background is required, but readers should be comfortable with Python and basic software concepts. What You Will Walk Away WithAfter reading this book, you will be able to:Design and implement an LLM system from scratchUnderstand how modern LLM products are structured internallyMake informed decisions about safety, governance, and deploymentConfidently evaluate AI systems beyond surface-level demosThis is not a shortcut book. It is a builder’s guide.If you want to understand how LLMs are actually built, operated, and maintained in the real world—this book is for you.

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