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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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Table of Contents (15) Chapters Close

Preface 1. Understanding the LLM Twin Concept and Architecture 2. Tooling and Installation FREE CHAPTER 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

Autoscaling capabilities to handle spikes in usage

So far, the SageMaker LLM microservice has used a static number of replicas to serve our users, which means that all the time, regardless of the traffic, it has the same number of instances up and running. As we highlighted throughout this book, machines with GPUs are expensive. Thus, we lose a lot of money during downtime when most replicas are idle. Also, if our application has sudden spikes in traffic, the application will perform poorly as the server cannot handle the number of requests. This is a massive problem for the user experience of our application, as in those spikes, we bring in the majority of new users. Thus, if they have a terrible impression of our product, we significantly reduce their chance of returning to our platform.

Previously, we configured our multi-endpoint service using the ResourceRequirements class from SageMaker. For example, let’s assume we requested four copies (replicas) with the following...

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