Supercharging business decisions with AI: Insight, optimize, and personalize to save $100M

Description

There are 15 million trips a day on Uber platform with 3M+ active drivers and 75M+ active riders across 700+ cities in its ride-hailing business. To operate at this scale, Uber needs systems that can continuously predict the demand and balance it with how Uber optimizes its spend and operational costs. Anu Gali’s team at Uber builds the systems that enable forecasting of how the markets will perform yearly and monthly. The team also generates insights for operational intelligence that drive Uber’s annual budget and determine the level of investment to be applied at each city and region across global markets.

The ML platform uses artificial neural network and recurrent neural network models to model each city; the company operates it as a microeconomic market with deep understanding on how Uber’s actions may influence the outcome in each of them. Uber enables the users to create various scenarios and choose the one that works best; it provides analytics on forecast versus actuals and enables continuously adjusting predictions to facilitate optimal outcomes. You’ll leave knowing about Uber’s big data, ML platform, and the various models used as well as some of the best practices on how to optimize those systems.