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LLM Engineer's Handbook

LLM Engineer's Handbook

By : Paul Iusztin, Maxime Labonne
4.8 (25)
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LLM Engineer's Handbook

LLM Engineer's Handbook

4.8 (25)
By: Paul Iusztin, Maxime Labonne

Overview of this book

Artificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems. Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects. By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.
Table of Contents (15 chapters)
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12
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Index

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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