HOW LYFT USES ML TO MAKE 100 MILLION PREDICTIONS A DAY

Summary from the TLDR Web Dev newsletter

Lyft processes 100 million ML predictions daily through LyftLearn Serving, a platform that replaced a problematic monolithic service with isolated microservices where each team owns their own model serving infrastructure. The system handles real-time predictions for ride pricing, fraud detection, and ETAs using a Flask/Gunicorn HTTP layer with custom predict/load functions that support any ML framework.