[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-70909":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":10,"archived":22,"fork":22,"defaultBranch":23,"hasWiki":24,"hasPages":24,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":29,"readmeContent":30,"aiSummary":31,"trendingCount":16,"starSnapshotCount":16,"syncStatus":32,"lastSyncTime":33,"discoverSource":34},70909,"machine-learning-systems-design","chiphuyen\u002Fmachine-learning-systems-design","chiphuyen","A booklet on machine learning systems design with exercises. NOT the repo for the book \"Designing Machine Learning Systems\", which is `dmls-book`","https:\u002F\u002Fhuyenchip.com\u002Fmachine-learning-systems-design\u002Ftoc.html",null,"HTML",10426,1615,303,8,0,14,15,59,42,100.03,false,"master",true,[26,27,28],"data-science","machine-learning-production","mlops","2026-06-12 04:00:58","# Machine Learning Systems Design\n\n**Read this booklet [here](https:\u002F\u002Fhuyenchip.com\u002Fmachine-learning-systems-design\u002Ftoc.html).**\n\n>>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](https:\u002F\u002Fwww.amazon.com\u002FDesigning-Machine-Learning-Systems-Production-Ready\u002Fdp\u002F1098107969) (O'Reilly, June 2022) is much more comprehensive and up-to-date. [The new book's repo](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fdmls-book) contains the full table of contents, chapter summaries, and random thoughts on MLOps tooling.\n\nThis booklet covers four main steps of designing a machine learning system:\n\n1. Project setup\n2. Data pipeline\n3. Modeling: selecting, training, and debugging\n4. Serving: testing, deploying, and maintaining\n\nIt 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.\n\nAt 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](https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fmachine-learning-systems-design\u002Ftree\u002Fmaster\u002Fanswers). You can read more about the book and sign up for the book's mailing list [here](https:\u002F\u002Fhuyenchip.com\u002F2019\u002F07\u002F21\u002Fmachine-learning-interviews.html).\n\n\n## Contribute\nThis is work-in-progress so any type of contribution is very much appreciated. Here are a few ways you can contribute:\n\n1. Improve the text by fixing any lexical, grammatical, or technical error\n1. Add more relevant resources to each aspect of the machine learning project flow\n1. Add\u002Fedit questions\n1. Add\u002Fedit answers\n1. Other\n\nThis book was created using the wonderful [`magicbook`](https:\u002F\u002Fgithub.com\u002Fmagicbookproject\u002Fmagicbook) 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:\n\n```\nbrew install node\n```\n\nInstall `magicbook` with:\n\n```\nnpm install magicbook\n```\n\nClone this repository:\n\n```\ngit clone https:\u002F\u002Fgithub.com\u002Fchiphuyen\u002Fmachine-learning-systems-design.git\ncd machine-learning-systems-design\n```\n\nAfter you've made changes to the content in the `content` folder, you can build the booklet by the following steps:\n\n```\nmagicbook build\n```\n\nYou'll find the generated HTML and PDF files in the folder `build`.\n\n## Acknowledgment\n\nI'd like to thank Ben Krause for being a great friend and helping me with this draft!\n\n\n## Citation\n","该项目是一本关于机器学习系统设计的小册子，包含练习题。核心功能包括介绍从项目设置、数据管道构建、模型选择与训练调试到服务部署与维护等四个主要步骤，并提供了实际资源链接和案例研究。技术特点上，它使用了`magicbook`包来生成内容，支持HTML和PDF格式的输出。适合希望了解或准备机器学习系统设计面试的读者，以及想要深入了解如何在生产环境中实施机器学习解决方案的数据科学家和工程师参考使用。",2,"2026-06-11 03:34:54","high_star"]