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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
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Alex Vesa
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Toc

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

Deploying the LLM Twin’s pipelines to the cloud

This section will show you how to deploy all the LLM Twin’s pipelines to the cloud. We must deploy the entire infrastructure to have the whole system working in the cloud. Thus, we will have to:

  1. Set up an instance of MongoDB serverless.
  2. Set up an instance of Qdrant serverless.
  3. Deploy the ZenML pipelines, container, and artifact registry to AWS.
  4. Containerize the code and push the Docker image to a container registry.

Note that the training and inference pipelines already work with AWS SageMaker. Thus, by following the preceding four steps, we ensure that our whole system is on the cloud, ready to scale and serve our imaginary clients.

What are the deployment costs?

We will stick to the free versions of the MongoDB, Qdrant, and ZenML services. As for AWS, we will mostly stick to their free tier for running the ZenML pipelines. The SageMaker training and inference components...

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