Uploaded June 2018 | Updated September 2026, 2 weeks ago
Uber is one of a growing group of Silicon Valley companies that uses ML to inform strategic business decisions. In this talk, data science manager Neha Gupta and data scientist Radhika Anand discuss how Uber’s Finance Data Science team uses deep learning, forecasting, and other ML methods for financial planning.
Uber is one of a growing group of Silicon Valley companies that uses ML to inform strategic business decisions. In this talk, data science manager Neha Gupta and data scientist Radhika Anand discuss how Uber’s Finance Data Science team uses deep learning, forecasting, and other ML methods for financial planning.
![[Uber Open Summit 2018] Scaling Ubers Big Data Platform](https://i.ytimg.com/vi/aveES6gn1Gs/mqdefault.jpg "[Uber Open Summit 2018] Scaling Ubers Big Data Platform
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/")
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During this May 2019 meetup in New York City, the Uber Visualization team gave a quick introduction to the new Uber Movement Street Speeds product and a brief overview of Kepler.gl.")
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During an August 15, 2018 React Native NYC meetup, Utility engineer Nick Koutrelakos discusses the importance of contributing high quality and informative React Native documentation as a means of continuing to nurture the growth of this open source community.")
![[Uber Seattle] Building Reliable Microservices @ Uber: An Introduction Prabhu Krishnamoorthy](https://i.ytimg.com/vi/c3RPjClWamY/mqdefault.jpg "[Uber Seattle] Building Reliable Microservices @ Uber: An Introduction Prabhu Krishnamoorthy
During this June 2018 meetup presentation, Uber engineering manager Prabhu Krishnamoorthy discusses how we build the tooling that powers our microservices and ensure that our distributed architecture is reliable, highly available, and scalable.")
![[MoneyCon 2019] Evolution of Revenue Optimization at Dropbox](https://i.ytimg.com/vi/cARulgmUtW8/mqdefault.jpg "[MoneyCon 2019] Evolution of Revenue Optimization at Dropbox
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, Dropboxs Kirill Sapchuk and Evgeny Skarbovsky discuss how revenue optimization (fighting involuntary churn) has evolved at Dropbox over the last few years from some simpler ideas and experiments around retries up to an ML-based approach that they are working on today.")
![[Uber Open Summit 2018] Building Your Own Metrics Pipeline with M3](https://i.ytimg.com/vi/cHx4DuqINTM/mqdefault.jpg "[Uber Open Summit 2018] Building Your Own Metrics Pipeline with M3
To facilitate the growth of Uber’s global operations, we need to be able to quickly store and access billions of metrics on our back-end systems. As part of our robust and scalable metrics infrastructure, we built M3, an open source metrics platform that has been in use at Uber for several years now. During this talk, engineer Nikunj Aggarwal walks through what a general metrics pipeline looks like and how M3 components can be used for the various stages of the pipeline without locking you in to a particular vendor. He also covers some of the unique challenges we face at Uber’s scale and how the learnings from those can be used by other companies.
Learn more about Uber Open Source: https://uber.github.io/")
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In this presentation from a May 2018 meetup, engineering manager Chintan Turakhia discusses how the Uber Marketplace leverages machine learning to deliver improved rider, driver, and eater experiences.")
![[XGBoost Meetup] Introduction to the XGBoost Community Nan Zhu & Philip Cho](https://i.ytimg.com/vi/dsqwIRrdcTA/mqdefault.jpg "[XGBoost Meetup] Introduction to the XGBoost Community Nan Zhu & Philip Cho
During a January 2019 XGBoost meetup hosted at Ubers SF office, Uber software engineer Nan Zhu and Amazon software engineer Philip Cho discuss the history and development of XGBoost, an open source software library that provides a gradient boosting framework for C++, Java, Python, R, and Julia.")
