This chapter presents all the essential tools that will be used throughout the book, especially in implementing and deploying the LLM Twin project. At this point in the book, we don’t plan to present in-depth LLM, RAG, MLOps, or LLMOps concepts. We will quickly walk you through our tech stack and prerequisites to avoid repeating ourselves throughout the book on how to set up a particular tool and why we chose it. Starting with Chapter 3, we will begin exploring our LLM Twin use case by implementing a data collection ETL that crawls data from the internet.

In the first part of the chapter, we will present the tools within the Python ecosystem to manage multiple Python versions, create a virtual environment, and install the pinned dependencies required for our project to run. Alongside presenting these tools, we will also show how to install the LLM-Engineers-Handbook repository on your local machine (in case you want to try out the code yourself...

Python ecosystem and project installation

Any Python project needs three fundamental tools: the Python interpreter, dependency management, and a task execution tool. The Python interpreter executes your Python project as expected. All the code within the book is tested with Python 3.11.8. You can download the Python interpreter from here: https://www.python.org/downloads/. We recommend installing the exact Python version (Python 3.11.8) to run the LLM Twin project using pyenv, making the installation process straightforward.

Instead of installing multiple global Python versions, we recommend managing them using pyenv, a Python version management tool that lets you manage multiple Python versions between projects. You can install it using this link: https://github.com/pyenv/pyenv?tab=readme-ov-file#installation.

After you have installed pyenv, you can install the latest version of Python 3.11, using pyenv, as follows:

pyenv install 3.11.8

Now list all installed Python...

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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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Pauline Nov 03, 2024

Great resource for those starting with large language models. It offers clear explanations of complex concepts, practical examples, and a wide range of topics, from data preparation to model deployment. Whether you're a technical expert or a curious learner, this book provides a solid foundation for understanding and working with LLMs.

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Amirhossein Oct 30, 2024

This book is an exceptional resource for anyone diving into the world of LLMs. I came in with a solid foundation in LLMs and the underlying transformer-based architecture, but I was eager to learn how to deploy my models effectively. This book deepens your understanding of LLMs and covers essential MLOps and LLMops practices, making it invaluable for engineers looking to bridge theory and practical deployment. Highly recommended for those wanting to take their LLM knowledge to the next level.

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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!

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!

Tans Oct 23, 2024

Excellent resource for anyone who is willing to build an end-to-end LLM from scratch. What makes it unique is the project-based approach where we are trying to build one LLM project from scratch and eventually deploy it.

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

What do you get with Print?

LLM Engineer's Handbook

Tooling and Installation

Python ecosystem and project installation

MLOps and LLMOps tooling

Databases for storing unstructured and vector data

MongoDB: NoSQL database

Preparing for AWS

Setting up an AWS account, an access key, and the CLI

Summary

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

What do you get with Print?

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Description

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What you will learn

Product Details

What do you get with Print?

Product Details

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Table of Contents

Recommendations for you

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