Uber’s Case Study at PAW Industry 4.0: Machine Learning to Enforce Mobile Performance
Data scientists, industrial planners, and other machine learning experts will meet at PAW in Las Vegas on June 16-20, 2019 to explore the latest trends and technologies in machine & deep learning for the IoT era.
Case Study: Machine Learning to Detect App Performance Regressions at Uber
In today’s digital age, users expect a fast, reliable mobile experience. Degradations (also referred to as regressions) in mobile app performance affect not only user experience, but even hurt business metrics. However, existing mobile app release pipelines lack the necessary infrastructure to detect regressions in a mobile app's performance before it is rolled out to the world. At Uber, we are building a state-of-the-art mobile regression detection pipeline, with the goal to detect regressions as small as 1%. Our approach includes both technological innovations as well as employing machine learning along with statistical testing techniques to improve the sensitivity of the regression experiments.
Companies on the 2019 Agenda
Watch PAW Founder Eric Siegel cover five reasons to attend:
PART OF MEGA-PAW – FIVE EVENTS IN ONE
- Automated Machine Learning with Python: A Case Study
- KDnuggets News, December 14: 3 Free Machine Learning Courses for…
- How is Machine Learning Beneficial in Mobile App Development?
- The Complete Machine Learning Study Roadmap
- How Uber manages Machine Learning Experiments with Comet.ml
- Deep Learning on your phone: PyTorch C++ API for use on Mobile Platforms