Cadence - search results
Uber engineers describe Cadence, Uber’s open source workflow orchestration tool, its architecture, and its use in a series of informative presentations.
Located in the heart of Latin America’s largest city, the Uber Sao Paulo Tech Center was founded in late 2018 as a company-wide hub...
Fostering a Culture of Sponsorship: Introducing Uber’s Engineering and Sponsorship Development Program
Designed by Uber's Office of the CTO, the Engineering Sponsorship and Development Program (ESDP) pairs participants with sponsors and provides an opportunity to hone technical leadership skills.
Managing multiple machine learning models to enable self-driving vehicles is a challenge. Uber ATG developed a model life cycle for quick iterations and a tool for continuous delivery and dependency management.
We built a backtesting service to better assess financial forecast model error rates, facilitating improved forecast performance and decision making.
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.
We share technical challenges and lessons learned while productionizing and scaling XGBoost to train distributed gradient boosted algorithms at Uber.
With the release of deck.gl version 7.3, Uber’s open source visualization tool now supports rendering massive geospatial data sets formatted according to the OGC 3D Tiles community standard.
Improving the User Experience with Uber’s Customer Obsession Ticket Routing Workflow and Orchestration Engine
Uber adopted workflow orchestration and Cadence, our open source orchestration engine, to better handle customer support ticket routing at scale.
Uber engineers offer two common use cases showing how we orchestrate machine learning model training in our data workflow engine.
DBEvents: A Standardized Framework for Efficiently Ingesting Data into Uber’s Apache Hadoop Data Lake
Uber engineers discuss the development of DBEvents, a change data capture system designed for high data quality and freshness that is capable of operating on a global scale.
In our continuing series about building our new driver app, Uber engineers discuss designing the architecture of the mobile app using RIBs, our open source mobile development framework.
Uber developed its own financial planning software, relying on data science and machine learning, to deliver on-demand forecasting and optimize strategic and operations decisions.
deck.gl v5 incorporates simplified APIs, scripting support, and framework agnosticism, making the popular open source data visualization software more accessible than ever before.
Uber Engineering's data science workbench (DSW) is an all-in-one toolbox that leverages aggregate data for interactive analytics and machine learning.
UberRUSH API Case Study Series #4.