Uploaded June 2018 | Updated September 2026, 2 weeks ago
Believe it or not, there are over 25 distinct ways to pay for services on the Uber platform. In this presentation, software engineer Caroline Van den Hauwe will take the audience on a journey through her experience building the monitoring and debugging tools that help keep Uber's payments systems safe and reliable.
Believe it or not, there are over 25 distinct ways to pay for services on the Uber platform. In this presentation, software engineer Caroline Van den Hauwe will take the audience on a journey through her experience building the monitoring and debugging tools that help keep Uber's payments systems safe and reliable.
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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.
Airbnb Payments has a distributed, service-oriented system for order management and payment processing, and consistency is a challenge in distributed systems. In this talk during MoneyCon 2019, Airbnbs Sophie Behr and John Chew discuss their companys transition to that service-oriented architecture and how they leveraged idempotency to make their systems robust and (eventually) consistent.")
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During a January 2019 XGBoost Meetup hosted at Ubers SF office, Uber software engineers Wayne Zhang and Gorkem Ozkaya discuss how we transform telematics datasets into a structured form using a combination of feature engineering, deep learning, and XGBoost.")
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During a May 2018 meetup, data science manager Eoin OMahony discusses how Uber builds technologies to optimize matching on out platform, thereby creating an elastic and efficient network.")
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During a May 2019 meetup in San Francisco, Uber engineer, Ankit Mehta explains how our Marketplace Intelligence team is building an efficient and scalable H3-based geospatial data platform to facilitate improved user experiences across our products.")
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Pyro is a deep probabilistic programming language built on PyTorch, a GPU-accelerated deep learning framework. Developed at Uber AI Labs by Noah Goodman and team, Pyro is used as a platform for research in modern Bayesian machine learning, where deep neural networks can be used both in models and in inference. To scale to large datasets and high-dimensional models, Pyro uses stochastic variational inference algorithms and probability distributions built on top of PyTorch. The Pyro team works closely with the PyTorch team and many open source collaborators to create a rich, stable toolset for probabilistic machine learning research. During this Uber Open Summit 2018 tech talk, AI Labs JP Chen and Fritz Obermeyer discuss how to use this tool and contribute to Pyros growing open source AI ecosystem.
Learn more about Uber Open Source: https://uber.github.io/")
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Two of the more salient trends in mapping over the past decade, especially when it comes to connected and automated vehicles, are precision and freshness. During a March 2019 Uber meetup, Mapboxs Tory Smith discusses how his team is building a location platform that enables the creation of living HD maps.")
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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, LinkedIns Tim Tan gives the audience an inside look at how LinkedIn approaches payment transaction routing, taking into consideration local payments, fallbacks, and experimentation.")
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During our May 30, 2018 Uber Mobility meetup, Uber engineer Leland Takamine discusses how his team built Nanoscope, a high performance method tracer we have successfully used to gather insight into performance bottlenecks of mobile applications.
Check out the Eng Blog article: http://eng.uber.com/nanoscope/
Try out the project: https://github.com/uber/nanoscope")
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Uber’s mission is to ignite opportunities by setting the world in motion. To fulfill this mission, Uber relies heavily on making data-driven decisions in every product area and we need to store and process an ever-increasing amount of data, in addition to providing faster, more reliable, and more-performant access. This talk will reflect on the challenges faced with scaling Uber’s Big Data Platform to ingest, store, and serve 100+ PB of data with minute level latency while efficiently utilizing our hardware.
Presenters:
Abhi Khune, Engineering Director at Uber: Abhi leads the Big Data team at Uber. His team is responsible for ingesting, storing, processing and surfacing data and insights for all of Uber’s diverse data analytics and ML requirements. Abhi is a software engineer at heart and has over 15 years of management and leadership experience.
Reza Shiftehfar, Engineering Manager at Uber: Reza Shiftehfar leads Uber’s Hadoop Platform team. His team helps build and grow Uber’s reliable and scalable Big Data platform that serves petabytes of data utilizing technologies such as Apache Hadoop, Apache Hive, Apache Kafka, Apache Spark, and Presto. Reza is one of the founding engineers of Uber’s data team and helped scale Ubers data platform from a few terabytes to over 100 petabytes while reducing data latency from 24+ hours to minutes.
Learn more about Uber Open Source: https://uber.github.io/")
