machine-learning-systems-design README

The project's README from github.com/chiphuyen/machine-learning-systems-design. As one markdown file: README.md.

Machine Learning Systems Design

Read this booklet here.

This booklet was my initial attempt to write about machine learning systems design back in 2019. My understanding of the topic has gone through significant iterations since then. My book Designing Machine Learning Systems (O'Reilly, June 2022) is much more comprehensive and up-to-date. The new book's repo contains the full table of contents, chapter summaries, and random thoughts on MLOps tooling.

This booklet covers four main steps of designing a machine learning system:

  1. Project setup
  2. Data pipeline
  3. Modeling: selecting, training, and debugging
  4. Serving: testing, deploying, and maintaining

It comes with links to practical resources that explain each aspect in more details. It also suggests case studies written by machine learning engineers at major tech companies who have deployed machine learning systems to solve real-world problems.

At the end, the booklet contains 27 open-ended machine learning systems design questions that might come up in machine learning interviews. The answers for these questions will be published in the book Machine Learning Interviews. You can look at and contribute to community answers to these questions on GitHub here. You can read more about the book and sign up for the book's mailing list here.

Contribute

This is work-in-progress so any type of contribution is very much appreciated. Here are a few ways you can contribute:

  1. Improve the text by fixing any lexical, grammatical, or technical error
  2. Add more relevant resources to each aspect of the machine learning project flow
  3. Add/edit questions
  4. Add/edit answers
  5. Other

This book was created using the wonderful magicbook package. For detailed instructions on how to use the package, see their GitHub repo. The package requires that you have node. If you're on Mac, you can install node using:

brew install node

Install magicbook with:

npm install magicbook

Clone this repository:

git clone https://github.com/chiphuyen/machine-learning-systems-design.git
cd machine-learning-systems-design

After you've made changes to the content in the content folder, you can build the booklet by the following steps:

magicbook build

You'll find the generated HTML and PDF files in the folder build.

Acknowledgment

I'd like to thank Ben Krause for being a great friend and helping me with this draft!

Citation

machine-learning-systems-design (chiphuyen/machine-learning-systems-design)

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Machine-learning-systems-design
这是什么？一本关于机器学习系统设计的免费手册，涵盖四个主要步骤：项目设定、数据流水线、建模（选择、训练、调试）和服务（测试、部署、维护），并附有实用资源和案例研究的链接。
这就是作者出版的《Designing Machine Learning Systems》吗？不是，这是更早也更短的 2019 年草稿手册。真正的 O'Reilly 图书对这个主题讲得深入得多，存放在另一个仓库（dmls-book）里。
到底怎么读这本手册？点 README 顶部的链接，在线阅读生成的 HTML/PDF 版本即可。
这些内容还在积极维护或扩充吗？没有，它是 2019 年的早期未完成草稿；此后作者的认识已经演进了很多，更成熟的材料现在都在已出版的图书里。
手册包含它提到的 27 道面试题吗？包含，手册收录了 27 道机器学习面试中常见的开放式机器学习系统设计题。
这 27 道题的答案在哪里找？作者的《Machine Learning Interviews》一书给出了官方答案，另一个链接的 GitHub 仓库则收集了社区贡献的答案。
既然已经出了完整的书，为什么还保留这本手册？它保留了催生后来那本更全面图书的 2019 年原始草稿，而且仍然是一份免费、更轻量的资源，带有精选链接和案例研究。
What are similar projects?machine-learning-interview, llm_interview_note, system-design-notes, professional-programming, SDE-Interview-and-Prep-Roadmap, awesome-mlops, system-design-interview, path-to-senior-engineer-handbook
Which topics is it in?production, ml, mlops, data-engineering, system-design, study-guide, deployment, interview, interview-preparation, best-practices, career, data-science
Creator
chiphuyen
Size
1.4 MB
Stars
10.7K
Updated
CommitJun 22
Age
6y
Commits · 90 Days