Tag: TensorFlow
No Coding Required: Training Models with Ludwig, Uber’s Open Source Deep Learning Toolbox
Uber AI's Piero Molino discusses Ludwig's origin story, common use cases, and how others can get started with this powerful deep learning framework built on top of TensorFlow.
Introducing Ludwig, a Code-Free Deep Learning Toolbox
Uber AI developed Ludwig, a code-free deep learning toolbox, to make deep learning more accessible to non-experts and enable faster model iteration cycles.
Horovod Joins the LF Deep Learning Foundation as its Newest Project
Horovod, Uber's distributed training framework, joins the LF Deep Learning Foundation to help advance open source innovation in AI, ML, and deep learning.
Open Source at Uber: Meet Alex Sergeev, Horovod Project Lead
We sat down with Horovod project lead, Alex Sergeev, to discuss his path to open source and what most excites him about the future of Uber's distributed deep learning framework.
Faster Neural Networks Straight from JPEG
Uber AI Labs introduces a method for making neural networks that process images faster and more accurately by leveraging JPEG representations.
NVIDIA: Accelerating Deep Learning with Uber’s Horovod
Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.
Scaling Machine Learning at Uber with Michelangelo
Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.
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.
