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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

Monolithic versus microservices architecture in model serving

In the previous section, we saw three different methods of deploying the ML service. The differences in architecture were mainly based on the interaction between the client and the ML service, such as the communication protocol, the ML service responsiveness, and prediction freshness.

But another aspect to consider is the architecture of the ML service itself, which can be implemented as a monolithic server or as multiple microservices. This will impact how the ML service is implemented, maintained, and scaled. Let’s explore the two options.

Figure 10.2: Monolithic versus microservices architecture in model serving

Monolithic architecture

The LLM (or any other ML model) and the associated business logic (preprocessing and post-processing steps) are bundled into a single service in a monolithic architecture. This approach is straightforward to implement at the beginning of a project, as everything...

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