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LLM Engineer's Handbook

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

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Product type Paperback
Published in Oct 2024
Publisher Packt
ISBN-13 9781836200079
Length 522 pages
Edition 1st Edition
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Authors (3):
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Maxime Labonne Maxime Labonne
Author Profile Icon Maxime Labonne
Maxime Labonne
Paul Iusztin Paul Iusztin
Author Profile Icon Paul Iusztin
Paul Iusztin
Alex Vesa Alex Vesa
Author Profile Icon Alex Vesa
Alex Vesa
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Toc

Table of Contents (15) Chapters Close

Preface 1. Understanding the LLM Twin Concept and Architecture FREE CHAPTER 2. Tooling and Installation 3. Data Engineering 4. RAG Feature Pipeline 5. Supervised Fine-Tuning 6. Fine-Tuning with Preference Alignment 7. Evaluating LLMs 8. Inference Optimization 9. RAG Inference Pipeline 10. Inference Pipeline Deployment 11. MLOps and LLMOps 12. Other Books You May Enjoy
13. Index
Appendix: MLOps Principles

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...

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