This is a book on "how to apply machine learning to production systems," designed to serve as a navigational map for software developers to organize their knowledge framework in the age of artificial intelligence. In terms of purpose, it is similar to my other work, The Fenix Project. However, this book has a higher barrier to entry. Before diving in, it is recommended to read the article On Designing Machine Learning Application to understand the book's scope and intended audience.

I wrote this book with the following goals in mind:

• I am a software engineer and a PhD candidate in machine learning. At a time when the software industry is shifting from "hardcoded logic" to "data-driven intelligence," I hope to use this book as a guide to systematically organize my machine learning knowledge, fill in the gaps, and integrate everything into my own knowledge framework.
• I believe there is a shortcut to technical growth: not only reading, studying, thinking, and applying, but also articulating and writing. Expressing what you believe you have mastered -- stating it clearly and coherently, explaining it with confidence, making others understand and resolving their doubts, and subjecting your views to the scrutiny and even challenge of others. In this process, you uncover much of the "unknown" lurking behind the "known." There is no true knowledge without action; knowledge without action is not yet true knowledge.

Designing Machine Learning Application Systems

• Read Online: The online reading address is https://ai.icyfenix.cn/en. The site is hosted on GitHub Pages; continuous integration powered by GitHub Actions compiles the Markdown documents from the Git repository and syncs them to the website in real time, pushing them to a CDN for accelerated access within China. Discussion services are provided by GitHub Discussions and Giscus.

• Code Sandbox: This book contains a large number of code exercises that can be run directly in the browser, used to demonstrate machine learning algorithms and to perform model training. For Part I (Mathematical Foundations) and Part II (Classical Statistical Learning Algorithms), a pure CPU environment is sufficient. For deep learning and beyond, some model training experiments require GPU support (currently based on PyTorch with CUDA 12.8). For more details, see Setting Up the Sandbox Environment.

  npx @icyfenix-dmla/install@latest

• Offline Use:

  • Deploy an offline site: The documentation is built with VuePress. To set up a documentation site within your organization, use the following commands:

  # Clone the source code
  git clone https://github.com/fenixsoft/dmla.git && cd dmla
  
  # Install project dependencies
  npm install
  
  # Start the website; the docs are served at http://localhost:8080 by default,
  # and the sandbox at http://localhost:3001
  npm run local

  • When running locally, executing the code in the documentation still requires Docker image support. You can pull images directly using the one-click sandbox installer mentioned above, or build them locally with:

  npm run build:sandbox:all

• Secondary Creation, Distribution, and Publishing: All content in this document cites sources where applicable, with rights belonging to the original authors. All other content, including but not limited to text, images, tables, etc., is original work by the author. This original content is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). As long as you follow the license terms of attribution, non-commercial use, and share-alike, you may use, modify, adapt, and distribute any part of this document anywhere, in any form, to anyone. See the "License" section of this document for details.

• This work is licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0). Under this license, you are free to share the material (copy and redistribute it in any medium or format) and to adapt it (remix, transform, and build upon it). However, you must:
  • Attribution: When using all or part of this book, you must credit the original author and provide source information.
  • Non-Commercial: You may not use the material for commercial publication or any other commercial purposes. For commercial use, please contact the author.
  • Share-Alike: If you remix, transform, or build upon this work, you must distribute your contributions under the same Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0).