Keynote: Inside a Self-Driving Uber

Location: Cyril Magnin Ballroom

Duration: 9:00am - 10:10am

Day of week: Tuesday

Abstract

Over the course of three years, Uber’s self-driving vehicles have driven over 2 million miles and have completed over 50,000 passenger trips in Pittsburgh and Phoenix. Many of you might be curious as to how we built a fleet of self-driving vehicles capable of driving autonomously in varying terrains and conditions. In this talk, Matt will break down the software components that come together to make a self-driving Uber drive itself. You’ll also learn about how we thoroughly test new software before it is deployed to the fleet.

Speaker: Matt Ranney

Sr. Staff Engineer @UberATG

Matt works on Uber ATG's simulation systems to exercise autonomy software before it gets onto the road. Before that, he worked on distributed systems, architecture, and performance at Uber.

Find Matt Ranney at

Tracks

  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.