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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 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

4. Testing

The same trend is followed when testing ML systems. Hence, we must test our application across all three dimensions: the data, the model, and the code. We must also ensure that the feature, training, and inference pipeline are well integrated with external services, such as the feature store, and work together as a system. When working with Python, the most common tool to write your tests is pytest, which we also recommend.

Test types

In the development cycle, six primary types of tests are commonly employed at various stages:

  • Unit tests: These tests focus on individual components with a single responsibility, such as a function that adds two tensors or one that finds an element in a list.
  • Integration tests: These tests evaluate the interaction between integrated components or units within a system, such as the data evaluation pipeline or the feature engineering pipeline, and how they are integrated with the data warehouse and feature store.
  • ...
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