How Uber Uses Deep Learning

Uber's blog post discusses their migration of machine learning workloads to Kubernetes using Ray, detailing the challenges faced with their previous setup and the improvements achieved with the new...

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

The article discusses the implementation of Two-Tower Embeddings (TTE) at Uber, highlighting its role in enhancing the efficiency and scalability of recommendation systems.

The article discusses Uber's Machine Learning (ML) Education Program, detailing its design, content delivery methods, and outcomes.

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

The article discusses the challenges and opportunities Uber faces in reducing the costs associated with its big data platform, which has grown significantly in scale and expense.

The article discusses the integration of Elastic Distributed Training with XGBoost on Ray, highlighting how this approach addresses challenges in distributed machine learning at scale.

The article discusses the application of relational graph learning, specifically relational graph convolutional networks (RGCN), to detect collusion in fraudulent activities within the Uber platfor...

The article discusses the integration of Elastic Horovod with Ray, focusing on how this combination enhances distributed deep learning training by enabling autoscaling and fault tolerance.

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

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 data mining operations aimed at identifying real-world pedestrian crossing scenarios to enhance machine learning for self-driving vehicles (SDVs).

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...

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 three innovative approaches to scaling machine learning at Uber, specifically from the Seattle Engineering team.

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

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 Second Uber Science Symposium showcased advancements in programming systems and tools, featuring talks from leading researchers and practitioners from institutions like MIT and Berkeley, as wel...

The Second Uber Science Symposium focused on advances in behavioral science, featuring presentations from leading researchers in the field.

The article announces the Uber Open Summit 2019, highlighting Uber's commitment to open source software and its global impact.

The article features an interview with Fritz Obermeyer and Noah Goodman from Uber AI, discussing the significance of Pyro, an open-source deep probabilistic programming language.

Pyro, a universal probabilistic programming language developed by Uber AI, has been accepted as a hosted project by the Linux Foundation Deep Learning Foundation.

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 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 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 introduces the Metropolis-Hastings GAN (MH-GAN), a novel approach to enhance Generative Adversarial Networks (GANs) by leveraging the discriminator for improved sample selection.

The article discusses highlights from the Uber Open Summit 2018, focusing on the importance of open source collaboration in various fields such as big data, machine learning, and front-end developm...

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

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 introduces Petastorm, an open-source data access library developed by Uber's Advanced Technologies Group (ATG) for facilitating deep learning model training and evaluation directly from...

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

The article discusses a novel meta-learning algorithm designed to improve the robustness of deep learning models by reweighting training examples based on their gradient directions.

The article discusses the limitations of Gaussian multiplicative noise as a regularization technique in neural networks and critiques its reinterpretation as Bayesian inference.

The article discusses a deep learning system designed to automatically identify Northern Leaf Blight (NLB) lesions in maize plants using field imagery.