Machine Learning at Uber

Sep 12, 2017 · San Francisco, United States of America

Uber is different to many tech companies in that its core mission is deeply entangled with the real-time physical world. We’ll talk about how this changes the flavor of machine learning that happens at Uber, why this makes Uber home to some of the most interesting machine learning problems ever faced, and why machine learning is critical to Uber’s future. 

Schedule:

6:00-6:20pm - Doors open, food, drinks, and networking

6:20-6:30pm- Welcome from M Waleed Kadous, Principal Engineer

6:30 - 6:45pm - Maps, M Waleed Kadous

Maps are the foundation that the Uber ecosystem operates on -- without maps, things like choosing the right car, assessing fares, selecting your destination and working out your location become impossible.

6:45-7:00pm- Marketplace, Chintan Turakhia

Marketplace is the brain behind Uber. To achieve optimal efficiency, Uber’s two-sided marketplace of riders and drivers requires real-time algorithms that can learn and adapt.

7:00pm -7:15pm - Fraud, Abhishek Gupta

Fraud and safety related incidents can detrimentally affect Uber’s reputation and customer experience, and they also increase the cost.

7:15-7:30pm - Data Science Platforms, Franziska Bell

Data Science Platforms at Uber play an integral part in enabling internal teams to build machine learning algorithms and perform experimentation at scale with rapid turn-around times.

7:30-8:30pm -Networking 

Please be sure to RSVP and looking forward seeing you there!


IMPORTANT:

This event is co-organized with Uber. To keep logistics simple, we're asking members to register for this event at:

https://www.meetup.com/UberEvents/events/242570359/

Event organizers
  • SF Bayarea Machine Learning

    A group to discuss all things Machine Learning! Topics may include (but are not limited to): learning theory, computer vision, natural language processing, data mining, computer audition, etc. Everyone is welcome! Videos of past meetups  • on Hakka Labs • on Vimeo Other local Machine Learning groups: https://www.noisebridge.net/index.php?title=Machine_Learning  

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