InfoQ Homepage Uber Content on InfoQ
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User Simulation for Rapid Outage Mitigation
Carissa Blossom walks through the monitoring service that Uber developed to identify issues in production at the individual city level all across the globe.
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Safe and Fast Deploys at Planet Scale
Mathias Schwarz discusses the software management, scalability used by Uber, and the need to have these done automatically by software.
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User Simulation for Rapid Outage Mitigation
Carissa Blossom walks through the monitoring service that Uber developed to identify issues in production, and how they leveraged composable integration tests to cut the time to mitigation in half.
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Culturing Resiliency with Data: a Taxonomy of Outages
Ranjib Dey overviews the categorization of outages that happened at Uber in the past few years based on root cause types.
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Self-Driving Cars as Edge Computing Devices
Matt Ranney explains the architecture of Uber ATG’s self-driving cars and takes a look at how the software is developed, tested, and deployed.
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Peloton - Uber's Webscale Unified Scheduler on Mesos & Kubernetes
Mayank Bansal and Apoorva Jindal present Peloton, a Unified Resource Scheduler for collocating heterogeneous workloads in shared Mesos clusters.
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Conquering Microservices Complexity @Uber with Distributed Tracing
Yuri Shkuro talks about how Uber is using distributed tracing to make sense of a large number of microservices and the interaction among them.
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Petastorm: A Light-Weight Approach to Building ML Pipelines
Yevgeni Litvin describes how Petastorm facilitates tighter integration between Big Data and Deep Learning worlds, simplifies data management and data pipelines, and speeds up model experimentation.
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Ludwig: A Code-Free Deep Learning Toolbox
Piero Molino introduces Ludwig, a deep learning toolbox that allows to train models and to use them for prediction without the need to write code.
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Michelangelo Palette: A Feature Engineering Platform at Uber
Amit Nene and Eric Chen discuss the infrastructure built by Uber for Michelangelo ML Platform that enables a general approach to Feature Engineering across diverse data systems.
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Michelangelo - Machine Learning @Uber
Jeremy Hermann talks about Michelangelo - the Machine Learning Platform that powers most of the machine learning solutions at Uber.
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Big Data and Deep Learning: A Tale of Two Systems
Zhenxiao Luo explains how Uber tackles data caching in large-scale DL, detailing Uber’s ML architecture and discussing how Uber uses Big Data, concluding by sharing AI use cases.
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