Сrowd-kit - a scikit-learn for crowdsourced annotations

Talk Information: Сrowd-kit - a scikit-learn for crowdsourced annotations
The talk includes the presentation of crowd-kit - an open-source computational quality control library - followed by its demonstration.
Crowdsourced annotations in most cases require post-processing due to their heterogeneous nature; raw data contains errors, is biased and non-trivial to combine. Crowd-kit provides various methods like aggregation, uncertainty, and agreements, which could be used as helping tools in getting an interpretable result out of data labeled with the help of crowdsourcing.

Speaker Bio Evgeniya Sukhodolskaya
Evgeniya is a Data Evangelist at Toloka: data labelling platform for machine learning pipelines, used world-wide by approximately 2,000 large and small businesses.
Her career path is made up of being an analyst-developer, an machine learning engineer, a solution architect and a business analyst, including 2 years experience of working with crowdsourcing. Evgeniya’s background is in Artificial Intelligence & Data Engineering, she’s currently doing her masters at Technical University of Munich.