How Uber Uses Embedding
12 engineering articles about Embedding from Uber's engineering team
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Uber’s Rate Limiting System details the evolution of Uber's approach to managing service overload through a unified rate-limiting architecture.
Chien-Chih Liao, Rahul Gutal, Smit Sheth, Ying Jiang
14 min read
Includes Code
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The article discusses Uber's transition from traditional keyword-based search using Apache Lucene to implementing semantic vector search with Amazon OpenSearch.
Hao Sun, Jiasen Xu, Smit Patel, Anand Kotriwal, Xu Zhang
11 min read
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The article discusses the evolution and scaling of Uber's Delivery Search Platform, emphasizing the transition from traditional lexical search to a semantic search model that enhances user experien...
Divya Nagar, Zheng Liu, Jiasen Xu, Bo Ling, Haoyang Chen
11 min read
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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...
Hugh Chen, Eric Wang, Gaoyuan Huang, Howard Yu, Jia Li, Sally Lee
14 min read
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This article discusses how Uber enhances personalized CRM communication using contextual bandit strategies, particularly focusing on the application of AI/ML techniques to optimize email content.
LJ (Lin) He, Yifeng Wu, Gaurav Jindal
13 min read
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The article discusses Genie, Uber's generative AI on-call copilot designed to enhance communication and efficiency in on-call operations.
Paarth Chothani, Eduards Sidorovics, Xiyuan Feng, Nicholas Marcott, Jonathan Li, Chun Zhu, Kailiang Fu, Meghana Somasundara
11 min read
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The article discusses DragonCrawl, a generative AI system developed by Uber to enhance mobile testing by mimicking human-like interactions with applications.
Juan Marcano, Mengdie Zhang, Ali Zamani, Anam Hira
18 min read
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The article discusses the implementation of Two-Tower Embeddings (TTE) at Uber, highlighting its role in enhancing the efficiency and scalability of recommendation systems.
Bo Ling, Melissa Barr, Dhruva Dixith Kurra, Chun Zhu, Nicholas Marcott
18 min read
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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.
Ankit Jain, Isaac Liu, Ankur Sarda, Piero Molino
18 min read
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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 ...
Carsten Jacobsen
7 min read
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The article discusses the scaling of Uber's Customer Support Ticket Assistant (COTA) system using deep learning techniques.
Huaixiu Zheng, Guoqin Zheng, Naveen Somasundaram, Basab Maulik, Hugh Williams, Jeremy Hermann
15 min read
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This article discusses the implementation of uncertainty estimation in neural networks for time series prediction at Uber, focusing on the use of Bayesian neural networks (BNNs) and the Monte Carlo...
Lingxue Zhu, Nikolay Laptev
21 min read
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