Machine Learning/fr

Welcome to the homepage of the Wikimedia Foundation's Machine Learning team.

Our purpose

 * Design, build, and maintain the foundation's machine learning infrastructure.
 * Plan, train, deploy, and manage production machine learning models created or requested by Wikimedia teams or Wiki communities.
 * Develop best practices for applied ethical machine learning.

Projects

 * Lift Wing - A scalable machine learning model serving infrastructure build off Kubeflow.
 * - Machine learning prediction as a web service. (See the list of tools that use ORES)
 * Wiki labels - Training interface where Wikipedians teach machines how to perform important tasks
 * revscoring - A machine prediction "scoring" framework for building prediction models used by ORES
 * [ Archived ] - Robust false-positive and feedback gathering system, to allow human refutation and review of ORES scoring.

What's new?
Interested in what we are working on at the moment?


 * Visit the talkpage for weekly summaries of the ML team
 * Join the bi-weekly call on Twitch, every other Thursday 17.30 UTC
 * Twitch video's are also available on the Wikimedia ML YouTube channel

Work with us
Have a question? Want to talk to the team or our community of volunteers about machine learning? Here are the best ways to connect with us.

Feedback on infrastructure
Would you be interested in providing feedback on the upcoming Lift Wing infrastructure? Let us know by signing up below, with your username and home-wiki.



Former staff and volunteers

 * とある白い猫 - IEG
 * Arthur Tilley - IEG
 * He7d3r - IEG/Volunteer
 * YuviPanda - Volunteer
 * Sumit - Research Intern
 * Ewitch - Research Intern
 * Marius Hoch - Software Engineer
 * Max Klein - Software Engineer
 * Adam Wight - Software Engineer
 * Natalia Timakova - Research Intern
 * Amir Sarabadani - Software Engineer
 * Nate TeBlunthuis - Research Intern
 * James Hare - Associate Product Manager
 * Aaron Halfaker - Principal Research Scientist & Team Lead
 * Habeeb Shopeju - Interne d'ingénierie logicielle