Uber Open Source

Videos

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  1 — [Uber Open Source] Using Apache Pinot at Scale
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  2 — [Uber Open Source] Understanding the History of Apache Pinot
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  3 — [Uber Open Source] Building City Cores with H3
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  4 — [Uber Open Source] Apache Zeppelin: Preview 0.9.0
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  5 — [Uber Open Source] Ludwig: A Code-free Deep Learning Toolbox
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  6 — [Uber Open Source] Petastorm: Uber ATG's Data Access Library for Deep Learning -- Yevgeni Litvin
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  7 — [Uber Open Source ] Distributed Deep Learning with Horovod -- Alex Sergeev
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  8 — [XGBoost Meetup] Machine Learning with Large-scale Telematics Data -- Wayne Zhang & Gorkem Ozkaya
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  9 — [StackUp] Open Source at Red Hat
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  10 — [Uber Open Source] New Features in Pinot: Standard SQL and Cloud Readiness
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  11 — [StackUp] vis.gl: Open source Tools for Large-scale Geospatial Data Visualization
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  12 — [XGBoost Meetup] Learning Deep Forest using XGBoost -- Yin Lou
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  13 — [StackUp] Open Sourcing Kaltura
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  14 — [StackUp] TimescaleDB: Performant Time-series Data Management and Analytics with PostgresSQL
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  15 — [Uber Open Source] Engineering an H3-based Geospatial Data Platform at Uber
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  16 — [XGBoost Meetup] Introduction to the XGBoost Community -- Nan Zhu & Philip Cho
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  17 — [Uber Open Source] Leveraging Pinot at Uber for Large-scale Analytics
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  18 — Piranha: An Open Source Tool to Automatically Delete Stale Code
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  19 — Ludwig: A Toolbox for Training and Testing Deep Learning Models without Writing Code
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  20 — [Uber Open Source] Engineering Sub-City Geos for a Hyper-Local Marketplace with Uber