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LAST UPDATED: JUNE 12, 2023

ML system design: 200 case studies to learn from

How do companies like Netflix, Airbnb, and Doordash apply machine learning to improve their products and processes? We put together a database of 200 case studies from 64 companies that share practical ML use cases and learnings from designing ML systems.

Navigation tips. You can play around with the database by filtering case studies by industry or ML use case. We added tags based on recurring themes. This is not a perfect or mutually exclusive division, but you can use the tags to quickly find:

  • ML systems with different data types: computer vision (CV) or natural language processing (NLP).
  • ML systems for specific use cases. The most popular are recommender systems, search and ranking, and fraud detection. 
  • We also labeled use cases where ML powers a specific user-facing "product feature": from grammatical error correction to generating outfit combinations.

Enjoy the reading! And if you find the database helpful, spread the word.

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Real-world ML systems

All the content belongs to respective parties. We simply put the links together

Selection criteria. Here is how we selected the case studies to include: 

  • It is a blog, paper, or article about a machine learning system created in-house (not by a vendor that sells or implements ML solutions for others).
  • It has sufficient detail on the ML use case and implementation: who the model is for, the ML model design, evaluation criteria, deployment architecture, etc. The more, the better. 
  • It covers a real-world ML system that is used in production.
  • It describes one particular ML use case. There are only a few exceptions that detail multiple ML projects at once — about causal ML or generative AI, for example.

Did we miss some great ML case studies? Let us know! Our Discord Community with 2000+ ML practitioners is the best place to share feedback.

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