Feature engineering is one of the most difficult and time-consuming aspects of a data scientist’s job, and the correct choice of features often determines whether or not a learning algorithm is effective. FeatureHub makes this step easier, faster, and more effective by collaboratively crowdsourcing feature creation via an open-source web notebook. FeatureHub gathers, tests, collates and extracts features submitted by users, and uses them to train relevant machine learning models. We are continuously improving FeatureHub’s usability, and plan to test models crowdsourced by it against those created through competition-based crowdsourcing.