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[P] Machine Learning Systems Design (open source book by @chipro)

An open source book compiled by Chip Huyen. Feel free to contribute:

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.

project: https://github.com/chiphuyen/machine-learning-systems-design

PDF: https://github.com/chiphuyen/machine-learning-systems-design/blob/master/build/build1/consolidated.pdf

submitted by /u/hardmaru
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.