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LLM Engineer's Handbook
LLM Engineer's Handbook

LLM Engineer's Handbook: Master the art of engineering large language models from concept to production

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Profile Icon Paul Iusztin Profile Icon Maxime Labonne
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$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (12 Ratings)
Paperback Oct 2024 522 pages 1st Edition
eBook
$47.99
Paperback
$59.99
Subscription
Free Trial
Renews at $12.99p/m
Arrow left icon
Profile Icon Paul Iusztin Profile Icon Maxime Labonne
Arrow right icon
$12.99 per month
Full star icon Full star icon Full star icon Full star icon Half star icon 4.8 (12 Ratings)
Paperback Oct 2024 522 pages 1st Edition
eBook
$47.99
Paperback
$59.99
Subscription
Free Trial
Renews at $12.99p/m
eBook
$47.99
Paperback
$59.99
Subscription
Free Trial
Renews at $12.99p/m

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Key benefits

  • Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning
  • Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production
  • Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications

Description

Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.

Who is this book for?

This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios

What you will learn

  • Implement robust data pipelines and manage LLM training cycles
  • Create your own LLM and refine it with the help of hands-on examples
  • Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring
  • Perform supervised fine-tuning and LLM evaluation
  • Deploy end-to-end LLM solutions using AWS and other tools
  • Design scalable and modularLLM systems
  • Learn about RAG applications by building a feature and inference pipeline

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Oct 22, 2024
Length: 522 pages
Edition : 1st
Language : English
ISBN-13 : 9781836200079
Vendor :
Amazon
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Product feature icon 50+ new titles added per month, including many first-to-market concepts and exclusive early access to books as they are being written.
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Product Details

Publication date : Oct 22, 2024
Length: 522 pages
Edition : 1st
Language : English
ISBN-13 : 9781836200079
Vendor :
Amazon
Category :
Languages :
Tools :

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.8
(12 Ratings)
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4 star 16.7%
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Robert Oct 27, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Before I read this book, I knew little about LLMs other than what the letters stood for. This book taught me a lot, and I know enough to start creating my own. The chapters are laid out well, and each chapter builds upon another. I can't recommend this book enough!
Amazon Verified review Amazon
Paul Gerber Oct 24, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
“LLM Engineer’s Handbook” by Paul Iusztin is a must-read for anyone passionate about data and machine learning! This book dives deep into the world of large language models, taking you from understanding the core concepts to deploying these models in production. I was excited to explore topics like model architecture, fine-tuning, and real-world applications, which were explained with such clarity and depth. The practical examples helped me immediately apply the knowledge to my own projects, and the insights on scaling and optimization were game-changers.Paul Iusztin does a fantastic job making complex topics accessible and engaging. Whether you’re just getting started or looking to sharpen your LLM skills, this book is an invaluable resource. I couldn’t put it down and left with a wealth of knowledge that will undoubtedly level up my engineering expertise. Highly recommend!
Amazon Verified review Amazon
Joseph C. Seroski Oct 23, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
If you haven't heard of LLM's yet, you better start learning about them if you are involved in IT, data engineering, or data analysis. This book is a great guide on setting up an LLM and understanding the architecture involved behind them. It's one of many books that you should have in your toolset.
Amazon Verified review Amazon
Stephan Miller Oct 23, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have been a software developer for two decades and I kind of have an idea about how AI works. I even ranked high in a stock market trading contest 3 times...by messing with configuration variables and features. I wasn't about to crack open the model or create my own to see how it actually worked. And by messing with configuration variables, I mean changing them arbitrarily and checking the results.This book with help take you from my level of "developing with AI" to the point you actually know what you are doing. There is more to AI that "prompt engineering" or using an AI model. A lot more.
Amazon Verified review Amazon
arsalan Oct 22, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Handy resource for the next big role in the AI Industry - LLM Engineer
Amazon Verified review Amazon
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