InfoQ Homepage Uber Content on InfoQ
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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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Inside a Self-driving Uber
Matt Ranney discusses the software components that come together to make a self-driving Uber drive itself, and how they test new software before it is deployed to the fleet.
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Machine Learning Pipeline for Real-Time Forecasting @Uber Marketplace
Chong Sun and Danny Yuan discuss how Uber is using ML to improve their forecasting models, the architecture of their ML platform, and lessons learned running it in production.
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Inside a Self-Driving Uber
Matt Ranney breaks down the software components that come together to make a self-driving Uber drive itself. He talks about how they thoroughly test new software before it is deployed to the fleet.
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Scaling Uber's Elasticsearch Clusters
Danny Yuan talks about how Uber scaled its Elasticsearch clusters as well as its ingestion pipelines for ingestions, queries, data storage, and operations by a three-person team.
