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

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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

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Shipping Details

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Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

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Premium: Trackable delivery to most EU destinations within 4-9 business days.

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India:

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Amazon verified reviews

Robert Oct 27, 2024

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

Paul Gerber Oct 24, 2024

“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!

Joseph C. Seroski Oct 23, 2024

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.

Stephan Miller Oct 23, 2024

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.

arsalan Oct 22, 2024

Handy resource for the next big role in the AI Industry - LLM Engineer

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

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

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