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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 Profile Icon Julien Chaumond Profile Icon Hamza Tahir
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$47.99
eBook Oct 2024 522 pages 1st Edition
eBook
$47.99
Paperback
$53.98 $59.99
Subscription
Free Trial
Renews at $19.99p/m
Arrow left icon
Profile Icon Paul Iusztin Profile Icon Maxime Labonne Profile Icon Julien Chaumond Profile Icon Hamza Tahir
Arrow right icon
$47.99
eBook Oct 2024 522 pages 1st Edition
eBook
$47.99
Paperback
$53.98 $59.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$47.99
Paperback
$53.98 $59.99
Subscription
Free Trial
Renews at $19.99p/m

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Table of content icon View table of contents Preview book icon Preview Book

LLM Engineer's Handbook

Tooling and Installation

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...

MLOps and LLMOps tooling

This section will quickly present all the MLOps and LLMOps tools we will use throughout the book and their role in building ML systems using MLOps best practices. At this point in the book, we don’t aim to detail all the MLOps components we will use to implement the LLM Twin use case, such as model registries and orchestrators, but only provide a quick idea of what they are and how to use them. As we develop the LLM Twin project throughout the book, you will see hands-on examples of how we use all these tools. In Chapter 11, we will dive deeply into the theory of MLOps and LLMOps and connect all the dots. As the MLOps and LLMOps fields are highly practical, we will leave the theory of these aspects to the end, as it will be much easier to understand it after you go through the LLM Twin use case implementation.

Also, this section is not dedicated to showing you how to set up each tool. It focuses primarily on what each tool is used for and highlights...

Databases for storing unstructured and vector data

We also want to present the NoSQL and vector databases we will use within our examples. When working locally, they are already integrated through Docker. Thus, when running poetry poe local-infrastructure-up, as instructed a few sections above, local images of Docker for both databases will be pulled and run on your machine. Also, when deploying the project, we will show you how to use their serverless option and integrate it with the rest of the LLM Twin project.

MongoDB: NoSQL database

MongoDB is one of today’s most popular, robust, fast, and feature-rich NoSQL databases. It integrates well with most cloud ecosystems, such as AWS, Google Cloud, Azure, and Databricks. Thus, using MongoDB as our NoSQL database was a no-brainer.

When we wrote this book, MongoDB was used by big players such as Novo Nordisk, Delivery Hero, Okta, and Volvo. This widespread adoption suggests that MongoDB will remain a leading NoSQL database...

Preparing for AWS

This last part of the chapter will focus on setting up an AWS account (if you don’t already have one), an AWS access key, and the CLI. Also, we will look into what SageMaker is and why we use it.

We picked AWS as our cloud provider because it’s the most popular out there and the cloud in which we (the writers) have the most experience. The reality is that other big cloud providers, such as GCP or Azure, offer similar services. Thus, depending on your specific application, there is always a trade-off between development time (in which you have the most experience), features, and costs. But for our MVP, AWS, it’s the perfect option as it provides robust features for everything we need, such as S3 (object storage), ECR (container registry), and SageMaker (compute for training and inference).

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

As AWS could change its UI/UX, the best way to instruct you on how to create an AWS account...

Summary

In this chapter, we reviewed the core tools used across the book. First, we understood how to install the correct version of Python that supports our repository. Then, we looked over how to create a virtual environment and install all the dependencies using Poetry. Finally, we understood how to use a task execution tool like Poe the Poet to aggregate all the commands required to run the application.

The next step was to review all the tools used to ensure MLOps best practices, such as a model registry to share our models, an experiment tracker to manage our training experiments, an orchestrator to manage all our ML pipelines and artifacts, and metadata to manage all our files and datasets. We also understood what type of databases we need to implement the LLM Twin use case. Finally, we explored the process of setting up an AWS account, generating an access key, and configuring the AWS CLI for programmatic access to the AWS cloud. We also gained a deep understanding of...

References

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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 : 9781836200062
Vendor :
Amazon
Category :
Languages :
Tools :

What do you get with eBook?

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Product feature icon Download this book in EPUB and PDF formats
Product feature icon Access this title in our online reader with advanced features
Product feature icon DRM FREE - Read whenever, wherever and however you want
Product feature icon AI Assistant (beta) to help accelerate your learning

Product Details

Publication date : Oct 22, 2024
Length: 522 pages
Edition : 1st
Language : English
ISBN-13 : 9781836200062
Vendor :
Amazon
Category :
Languages :
Tools :

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Table of Contents

13 Chapters
Understanding the LLM Twin Concept and Architecture Chevron down icon Chevron up icon
Tooling and Installation Chevron down icon Chevron up icon
Data Engineering Chevron down icon Chevron up icon
RAG Feature Pipeline Chevron down icon Chevron up icon
Supervised Fine-Tuning Chevron down icon Chevron up icon
Fine-Tuning with Preference Alignment Chevron down icon Chevron up icon
Evaluating LLMs Chevron down icon Chevron up icon
Inference Optimization Chevron down icon Chevron up icon
RAG Inference Pipeline Chevron down icon Chevron up icon
Inference Pipeline Deployment Chevron down icon Chevron up icon
MLOps and LLMOps Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon
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