How Uber Uses TensorFlow

This article discusses how Uber has integrated explainability into its machine learning platform, Michelangelo, using Integrated Gradients (IG) to provide interpretable attributions for deep learni...

The article discusses Uber's evolution in machine learning (ML) through its centralized platform, Michelangelo, highlighting its transition from predictive to generative AI.

Uber’s Real-Time Document Check is a critical feature designed to enhance rider identity verification and improve safety for drivers.

The article discusses DeepETA, Uber's advanced model for predicting arrival times using deep learning techniques.

Horovod v0. 21 introduces significant enhancements aimed at optimizing network utilization for distributed deep learning training.

Ludwig version 0. 3 introduces significant enhancements, including hyperparameter optimization, support for Transformers, and integration with TensorFlow 2.

The article introduces Neuropod, an open-source deep learning inference engine developed by Uber's Advanced Technologies Group (ATG).

The article discusses Uber ATG's machine learning infrastructure and versioning control platform, VerCD, designed to manage the complexities of developing self-driving vehicles.

The article discusses Uber's SpeedsUp visualization project, which utilizes machine learning to analyze and display city street speed patterns.

The article discusses Uber's open source initiatives in 2019, highlighting the company's contributions to the open source community, the establishment of the Open Source Program Office (OSPO), and ...

The article provides a comprehensive overview of the highlights from the Uber Engineering Blog in 2019, showcasing significant advancements and contributions in various technical domains such as ma...

In 2019, Uber AI advanced its mobility services through innovative applications of artificial intelligence across various domains, including computer vision, natural language processing, and deep l...

The article discusses how Uber Eats utilizes graph learning techniques to enhance its food recommendation system, improving user experience by providing personalized dish and restaurant suggestions.

The article discusses the evolution of the Michelangelo model representation at Uber to enhance flexibility and scalability in machine learning model serving.

The article introduces Hypothesis GU Func, an open-source Python package designed to facilitate unit testing for machine learning models, particularly those using NumPy and PyTorch.

Ludwig v0. 2 introduces significant enhancements to its deep learning toolbox, including new features such as Comet.

EvoGrad is a lightweight Python library designed to facilitate gradient-based evolution in machine learning.

The article introduces the Plato Research Dialogue System, a flexible conversational AI platform developed by Uber AI.

The article discusses Ludwig, an open-source deep learning toolbox developed by Uber that enables users to train machine learning models without writing code.

The article discusses how Uber utilizes a data workflow management system called Piper to enhance accessibility and efficiency in machine learning (ML) processes.

Uber has announced the open sourcing of Peloton, its unified resource scheduler designed to manage resources across various workloads efficiently.

The article discusses the latest updates to Horovod, a distributed deep learning framework, which now includes support for PySpark and Apache MXNet, along with features aimed at enhancing training ...

The article discusses the modeling of censored time-to-event data using Pyro, an open-source probabilistic programming language.

The First Uber Science Symposium brought together experts from various fields to discuss advancements in reinforcement learning (RL), natural language processing (NLP), conversational AI, and deep ...

The article introduces Ludwig, an open-source deep learning toolbox developed by Uber that allows users to train and test deep learning models without writing code.

The article discusses Manifold, a model-agnostic visual debugging tool developed by Uber for enhancing machine learning model performance.

The article reviews Uber's open source initiatives in 2018, highlighting the diversity of projects and their impact on the technical community.

The article introduces Alex Sergeev, the lead of the Horovod project at Uber, detailing the motivations behind open sourcing Horovod, a distributed deep learning framework.

Horovod, Uber's open-source distributed training framework, has joined the LF Deep Learning Foundation, enhancing its support for open-source innovation in AI and deep learning.

The article discusses an innovative approach to enhance the performance of Convolutional Neural Networks (CNNs) by utilizing JPEG's internal representations.

The article discusses how NVIDIA leverages Uber's Horovod to enhance the training of deep learning models for autonomous vehicles.

The article discusses the evolution and scaling of Uber's machine learning platform, Michelangelo, highlighting its development, deployment, and operational strategies.

The article discusses Peloton, Uber's unified resource scheduler designed to manage diverse cluster workloads efficiently.

The article highlights seven open source projects showcased at the Uber Open Summit, emphasizing Uber's contributions to the open source community.

The article introduces Michelangelo PyML, Uber's platform designed for rapid Python machine learning model development.

The article discusses how Uber leverages Natural Language Processing (NLP) and machine learning to enhance its map data quality by analyzing customer feedback.

The article announces the Uber Open Summit 2018, emphasizing the importance of open source collaboration in Uber's growth.

The article discusses the scaling of Uber's Customer Support Ticket Assistant (COTA) system using deep learning techniques.

The article discusses Uber's innovative hybrid Exponential Smoothing-Recurrent Neural Network (ES-RNN) model that won the M4 Forecasting Competition.

The article discusses advancements in deep neuroevolution, particularly how researchers can now train deep neural networks to play Atari games in approximately four hours on a single modern desktop...

Omphalos is Uber's innovative time series backtesting tool designed to enhance forecasting accuracy and model comparison across various programming languages.

The article discusses SBNet, an open-source algorithm developed by Uber ATG that leverages activation block sparsity to enhance the speed of Convolutional Neural Networks (CNNs).

The article highlights Uber's contributions to the open source community in 2017, showcasing several key projects that enhance software development and engineering practices.

The article introduces Michelangelo, Uber's internal machine learning platform designed to democratize machine learning and streamline the process of building, deploying, and operating ML solutions...