Data science (DS) projects often follow a \textit{lifecycle} that consists of laborious \textit{tasks} for data scientists and domain experts (e.g., data exploration, model training, etc.). Only till recently, machine learning(ML) researchers have developed promising automation techniques to aid data workers in these tasks. This paper introduces \textbf{AutoDS}, an automated machine learning (AutoML) system that aims to leverage the latest ML automation techniques to support data science projects. Data workers only need to upload their dataset, then the system can automatically suggest ML configurations, preprocess data, select algorithm, and train the model. These suggestions are presented to the user via a web-based graphical user interface and a notebook-based programming user interface. We studied AutoDS with 30 professional data scientists, where one group used AutoDS, and the other did not, to complete a data science project. As expected, AutoDS improves productivity; Yet surprisingly, we find that the models produced by the AutoDS group have \textbf{higher quality} and \textbf{less errors}, but \textbf{lower human confidence scores}. We reflect on the findings by presenting design implications for incorporating automation techniques into human work in the data science lifecycle.
https://doi.org/10.1145/3411764.3445526
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