Tag: TensorFlow
Introducing Petastorm: Uber ATG’s Data Access Library for Deep Learning
Uber's Advanced Technologies Group introduces Petastorm, an open source data access library enabling training and evaluation of deep learning models directly from multi-terabyte datasets in Apache Parquet format.
M4 Forecasting Competition: Introducing a New Hybrid ES-RNN Model
With a solid margin, Uber senior data scientist Slawek Smyl won the M4 Competition with his hybrid Exponential Smoothing-Recurrent Neural Networks (ES-RNN) forecasting method.
Omphalos, Uber’s Parallel and Language-Extensible Time Series Backtesting Tool
Uber Engineering created Omphalos, our new backtesting framework, to enable efficient and reliable comparison of forecasting models across languages.
SBNet: Leveraging Activation Block Sparsity for Speeding up Convolutional Neural Networks
Uber ATG Toronto developed Sparse Blocks Network (SBNet), an open source algorithm for TensorFlow, to speed up inference of our 3D vehicle detection systems while lowering computational costs.
Year in Review: 2017 Highlights from Uber Open Source
As we approach the New Year, Uber Open Source revisits some of Uber Engineering's most popular projects from 2017.
Engineering More Reliable Transportation with Machine Learning and AI at Uber
In this article, we highlight how Uber leverages machine learning and artificial intelligence to tackle engineering challenges at scale.
Turbocharging Analytics at Uber with our Data Science Workbench
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
Meet Horovod: Uber’s Open Source Distributed Deep Learning Framework for TensorFlow
Uber Engineering introduces Horovod, an open source framework that makes it faster and easier to train deep learning models with TensorFlow.
Meet Michelangelo: Uber’s Machine Learning Platform
Uber Engineering introduces Michelangelo, our machine learning-as-a-service system that enables teams to easily build, deploy, and operate ML solutions at scale.
