[Research] A visual deep dive into Uber's machine learning solution for predicting ETAs.

TL;DR: Uber follows a 2-layer approach. They combine traditional graph algorithms like Dijkstra with learned embeddings and a lightweight self-attention neural network to reliably predict estimated time of arrival or ETA.

[How Uber uses ML to ETAs (and solve a billion dollar problem)](https://open.substack.com/pub/codecompass00/p/uber-billion-dollar-problem-predicting-eta?r=rcorn&utm_campaign=post&utm_medium=web)

https://preview.redd.it/2ovttr82i9xc1.png?width=1358&format=png&auto=webp&s=51b12261bf98f529fd0e9b33daf6362b727f4580

algorithms attention deep dive embeddings graph graph algorithms layer machine machine learning machinelearning network neural network research self-attention solution uber visual