  The popularity of automated machine learning (AutoML) tools in different
domains has increased over the past few years. Machine learning (ML)
practitioners use AutoML tools to automate and optimize the process of feature
engineering, model training, and hyperparameter optimization and so on. Recent
work performed qualitative studies on practitioners' experiences of using
AutoML tools and compared different AutoML tools based on their performance and
provided features, but none of the existing work studied the practices of using
AutoML tools in real-world projects at a large scale. Therefore, we conducted
an empirical study to understand how ML practitioners use AutoML tools in their
projects. To this end, we examined the top 10 most used AutoML tools and their
respective usages in a large number of open-source project repositories hosted
on GitHub. The results of our study show 1) which AutoML tools are mostly used
by ML practitioners and 2) the characteristics of the repositories that use
these AutoML tools. Also, we identified the purpose of using AutoML tools (e.g.
model parameter sampling, search space management, model
evaluation/error-analysis, Data/ feature transformation, and data labeling) and
the stages of the ML pipeline (e.g. feature engineering) where AutoML tools are
used. Finally, we report how often AutoML tools are used together in the same
source code files. We hope our results can help ML practitioners learn about
different AutoML tools and their usages, so that they can pick the right tool
for their purposes. Besides, AutoML tool developers can benefit from our
findings to gain insight into the usages of their tools and improve their tools
to better fit the users' usages and needs.
