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Profiles in Coding: Rick Boone, Core Infrastructure, San Francisco

San Francisco skyline
Rick Boone, Strategic Advisor for Uber's Core Infrastructure group, talks about his journey from his work in site reliability to his current role in long-term planning for infrastructure health and scalability.

Tricks of the Trade: Tuning JVM Memory for Large-scale Services

latency graph
Uber engineers share their learnings on how to tune a Java Virtual Machine so as to avoid long pauses and other issues with garbage collection.

Building the Future of Mobility from the Pacific Northwest: Meet the Uber Seattle Tech Team

The Uber Seattle Tech team is responsible for building a diverse range of technologies, from developer tools to our data platform architecture.

Building a Backtesting Service to Measure Model Performance at Uber-scale

We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.

Counting Calories: How We Improved the Performance and Developer Experience of UberEats.com

Screenshots from UberEats.com
Improving the performance and developer velocity for the Uber Eats web application involved a complete rewrite, developing a new architecture and using Fusion.js.

Women in Data Science at Uber: Moving the World With Data in 2020—and Beyond

In October 2019, Uber hosted our second annual Moving The World With Data meetup, showcasing some of our most interesting data science challenges in 2019.

Designing a Production-Ready Kappa Architecture for Timely Data Stream Processing

elevated freeways
We implemented a Kappa architecture at Uber to effectively backfill streaming data at scale, ensuring accurate data in our platform.

Engineering SQL Support on Apache Pinot at Uber

We engineered full SQL support on Apache Pinot to enable quick analysis and reporting on aggregated data, leading to improved experiences on our platform.

Open Sourcing Manifold, a Visual Debugging Tool for Machine Learning

First introduced by Uber Engineering in January 2019, Manifold is a visual debugging tool that enables users to quickly identify performance issues in machine learning models.

Uber Visualization Highlights: Displaying City Street Speed Clusters with SpeedsUp

San Francisco map showing average, clustered traffic speeds
As part of Uber Visualization's all-team hackathon, we built SpeedsUp, a project using machine learning to process average speeds across a city, cluster the results, and overlay them on a street map.

Uber Open Source in 2019: Community Engagement and Contributions

Uber open source logo
Uber recounts its many engagements with the open source community during 2019, from contributing projects to joining and founding new open source support organizations.

Year in Review: 2019 Highlights from the Uber Engineering Blog

To cap off 2019, the Uber Engineering Blog editors present a selection of our most popular articles covering a range of technical topics, from AI to mobile development.

Uber Infrastructure in 2019: Improving Reliability, Driving Customer Satisfaction

In 2019, Uber's Infrastructure team built new services and systems to enable resource savings, efficiency gains, and greater resilience across our technology stack.

Uber AI in 2019: Advancing Mobility with Artificial Intelligence

Uber AI in 2019: Advancing Mobility with Artificial Intelligence
In 2019, Uber AI built tools and systems that leverage ML to improve location accuracy and enhance real-time forecasting, among other applications on our platform.

Generative Teaching Networks: Accelerating Neural Architecture Search by Learning to Generate Synthetic Training Data

Developed by Uber AI Labs, Generative Teaching Networks (GTNs) automatically generate training data, learning environments, and curricula to help AI agents rapidly learn.

Uber’s Data Platform in 2019: Transforming Information to Intelligence

In 2019, Uber's Data Platform team leveraged data science to improve the efficiency of our infrastructure, enabling us to compute optimum datastore and hardware usage.

Productionizing Distributed XGBoost to Train Deep Tree Models with Large Data Sets at Uber

We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.

Controlling Text Generation with Plug and Play Language Models

Plug and Play Language Model, introduced by Uber AI Labs, gives NLP practitioners the flexibility to plug in one or more simple attribute models into a large, unconditional language model.

Food Discovery with Uber Eats: Using Graph Learning to Power Recommendations

By integrating graph learning techniques with our Uber Eats recommendation system, we created a more seamless and individualized user experience for eaters on our platform.

Uber Goes to NeurIPS 2019

Uber is presenting 11 papers at the NeurIPS 2019 conference in Vancouver, Canada, as well as sponsoring workshops including Women in Machine Learning (WiML) and Black in AI.

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