A visual deep dive into Uber's ML system to solve the billion dollar problem of predicting ETAs.

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

[How Uber uses ML to ETAs](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/cg6r82se67xc1.png?width=1358&format=png&auto=webp&s=4ac9e946b30d858721b842f0f4407dfa6c50ce3e

algorithms attention billion deep dive deeplearning embeddings graph graph algorithms layer network neural network self-attention solve uber visual