650 Ways Companies Actually Use ML

What it does This repository is a curated markdown table of 650 machine learning case studies sourced from engineering blogs across over 100 companies. Each entry links to the original write-up and tags it by technique—LLM, RAG, recommender system, generative AI—and industry. It is essentially a single-page bibliography for production ML.

The interesting bit The README offers no original analysis; its value is pure aggregation. By repackaging Evidently AI’s original compilation into a flat, browsable list, it turns scattered corporate posts into a quick reference. The 2025 entries alone span Uber’s GenAI code review, Pinterest’s embedding-based retrieval, and Slack’s enterprise RAG search.

Key highlights

• 650 entries from companies including Netflix, Airbnb, and Uber
• Tagged by technique and year, with direct links to source material
• Heavy emphasis on recent LLM, RAG, and AI agent deployments
• Acknowledges Evidently AI as the original compiler
• Zero dependencies; it is a reference document, not a framework

Caveats

• The repo is a mirror of Evidently AI’s work, not an independent compilation.
• Discovery is limited to GitHub’s raw markdown table rendering; there is no search or structured query interface.

Verdict Useful for engineers prepping for system design discussions or mapping how industry actually deploys ML. Skip it if you are looking for code to run or novel research.