Tag: Uber Engineering

The Billion Data Point Challenge: Building a Query Engine for High Cardinality Time Series...

Part of Uber's open source M3 metrics system, our query engine can support real-time, large-scale computation and multiple query languages.

Introducing Makisu: Uber’s Fast, Reliable Docker Image Builder for Apache Mesos and Kubernetes

Uber built Makisu, our open source Docker image builder, to enable the quick, reliable generation of Dockerfiles in Mesos and Kubernetes ecosystems.

Uber Joins the Linux Foundation’s OpenChain Project as a Platinum Member

As part of the OpenChain Project’s governing board, Uber will help create best practices and define standards for open source software compliance.

Engineering Uber’s Next-Gen Payments Platform

During a September 2018 meetup, Uber's Payments Platform team discusses how this technology supports our company's growth through an active-active architecture, exactly-once payment processing, and scalability across businesses.

How to Get a Better GAN (Almost) for Free: Introducing the Metropolis-Hastings GAN

Metropolis-Hastings Generative Adversarial Networks (GANs) leverage the discriminator to pick better samples from the generator after ML model training is done.

Observability at Scale: Building Uber’s Alerting Ecosystem

Uber’s Observability team built a robust, scalable metrics and alerting pipeline to detect, mitigate, and notify engineers of issues as they occur.

Uber Joins the Linux Foundation as a Gold Member

Announced during the Uber Open Summit 2018, we extend our commitment to open source by joining the Linux Foundation as a Gold Member.

NVIDIA: Accelerating Deep Learning with Uber’s Horovod

Horovod, Uber's open source distributed deep learning system, enables NVIDIA to scale model training from one to eight GPUs for their self-driving sensing and perception technologies.

Engineering Sustainability: An Interview with Uber’s Head of Information Technology, Shobhana Ahluwalia

We sat down with Uber's Head of Information Technology to discuss her journey to tech services, what she finds most challenging about her work at Uber, and how her team is setting the company up for success.

Scaling Machine Learning at Uber with Michelangelo

Uber built Michelangelo, our machine learning platform, in 2015. Three years later, we reflect our journey to scaling ML at Uber and lessons learned along the way.

Peloton: Uber’s Unified Resource Scheduler for Diverse Cluster Workloads

Uber developed Peloton to help us balance resource use, elastically share resources, and plan for future capacity needs.

Michelangelo PyML: Introducing Uber’s Platform for Rapid Python ML Model Development

Uber developed Michelangelo PyML to run identical copies of machine learning models locally in both real time experiments and large-scale offline prediction jobs.
Photo of Uber app showing map

Applying Customer Feedback: How NLP & Deep Learning Improve Uber’s Maps

To improve our maps, Uber Engineering analyzes customer support tickets with natural language processing and deep learning to identify and correct inaccurate map data.

Seven Things to Know about Technical Writing at Uber

Technical writer and former intern Shannon Brown explains her work and answers common questions about this important role in Uber’s engineering organization.

Announcing Uber Open Summit 2018: Collaboration at Scale

Keynote speakers include Jim Zemlin, executive director of the Linux Foundation, and Zoubin Ghahramani, chief scientist at Uber AI Labs.

Improving Driver Communication through One-Click Chat, Uber’s Smart Reply System

One-click chat, the Uber driver app's smart reply system, leverages machine learning to make in-app messaging between driver-partners and riders more seamless.
Food Discovery with Uber Eats: Recommending for the Marketplace

Food Discovery with Uber Eats: Recommending for the Marketplace

Uber Eats engineers describe how they surface restaurant recommendations in the app using multi-objective optimization to give eaters the most satisfying experience while maintaining the health of the Uber Eats marketplace.

Under the Hood of Uber’s Experimentation Platform

Uber's experimentation platform empowers us to improve the customer experience by allowing teams to launch, debug, measure, and monitor product changes.

Scaling Uber’s Customer Support Ticket Assistant (COTA) System with Deep Learning

Uber built the next generation of COTA by leveraging deep learning models, thereby scaling the system to provide more accurate customer support ticket predictions.

Seeing Double: Meet Uber’s Identical Twin Data Scientists

Afshine and Shervine Amidi, identical twins, discuss their journeys to data science and how their work at Uber helps teams improve user experiences on our platform.

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