Uploaded June 2018 | Updated September 2026, 2 weeks ago
Accurate and reliable ETAs (Estimated Time of Arrival) are central to a seamless and enjoyable rider experience. In this talk, data scientist Sreeta Gorripaty will discuss how Uber predicts ETAs via routing algorithms, traffic estimation, and machine learning models.
Accurate and reliable ETAs (Estimated Time of Arrival) are central to a seamless and enjoyable rider experience. In this talk, data scientist Sreeta Gorripaty will discuss how Uber predicts ETAs via routing algorithms, traffic estimation, and machine learning models.
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During this Uber Open Summit Sofia 2019 presentation, engineer Celina Ward discusses why we built M3, our open source metrics stack, and M3DB, our open source distributed time series database. She also walks through how to use M3 and the future of its development roadmap.
Learn more about Uber Open Summit Sofia 2019: https://uberopensofia.splashthat.com/")
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During this May 2019 meetup in New York City, the Uber Visualization team offers a basic tutorial on how to download the Uber Movement Street Speeds dataset and visualize it in Kepler.gl. Through this tutorial, they explore spatiotemporal insights through the lens traffic speed data during two different time periods.")
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During this April 2019 meetup, Uber engineer Mingshi Wang discusses the architectures of Michelangelo (MA) learners and transformers. A Michelangelo (MA) Learner is a workflow authoring framework for Ubers Michelangelo machine learning platform that allows Uber data scientists, researchers, and engineers to solve complex machine learning problems with customized workflows. An MA Learner provides Python SDKs that make it easy to write MA models on Jupiter notebooks while hiding the underlying complexities of distributing the machine learning jobs to different computing environments. At Uber, machine learning models are represented by pipelines composed of MA transformers. The data preparation and training processes that involve one or more estimators produce a trained pipeline with MA transformers. The trained pipelines are persisted for subsequent usage by batch and online predictions.")
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During a May 2018 meetup, engineer manager Vivek Trehan discusses how we can improve features and services on our platform using our experimentation platform.
Learn more about our experimentation platform: https://eng.uber.com/xp/")
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The domain of Payments, Finance, and more generally FinTech, is a fast-growing industry that reached record global investment of $111.8B in 2018. The underlying technology that powers this incredible growth is also evolving rapidly. Hosted by Uber, MoneyCon 2019 brought together engineers from leading tech companies to present on the latest topics in payments engineering.
In this talk during MoneyCon 2019, Squares Anthony Bishopric discusses how Square applied a novel cryptographic concept and classic double entry accounting principles to process and disburse hundreds of millions of dollars per day.")
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During this July 2019 meetup in San Francisco, Alex Sexton from Stripe discusses how the web ecosystem of 2019 leaves plenty of vulnerable surface area to worry about.")
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During a September 2019 meetup hosted at Ubers NYC Engineering Office, Peter Mattis, co-founder of Cockroach Labs, discusses how distributed transactions work and explains the benefits of parallel commits through foundational computer science theory.")
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During Ubers September 2019 Visualization Nights meetup, SharedStreet engineer Morgan Herlocker introduces SharedStreets and discusses how their open source software enables public-private collaboration around and the exchange of transportation data. In this talk, he also demonstrates how to link Uber Movement data with OpenStreetMap and load the data set for visualization in Kepler.gl")
![[Uber Open Source] Petastorm: Uber ATGs Data Access Library for Deep Learning Yevgeni Litvin](https://i.ytimg.com/vi/J5SILwWOktM/mqdefault.jpg "[Uber Open Source] Petastorm: Uber ATGs Data Access Library for Deep Learning Yevgeni Litvin
During a March 2019 meetup hosted at Ubers SF office, Uber ATG staff software engineer Yevgeni Litvin discusses how data produced and managed by Big Data systems like Apache Spark and Apache Hive cannot be directly consumed by Deep Learning systems like TensorFlow and PyTorch.")
