Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.
Responsible for cleaning, storing, and serving over 100 petabytes of analytical data, Uber's Hadoop platform ensures data reliability, scalability, and ease-of-use with minimal latency.
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
Seemingly small inefficiencies are greatly magnified as Uber's business scales. In this article we’ll explore design considerations and unique implementation characteristics of Pyflame, Uber Engineering's high-performance Python profiler implemented in C++.