“How do companies like Netflix, Airbnb, and Doordash apply machine learning to improve their products and processes? We put together a database of 300 case studies from 80+ 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.

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