  There has been considerable growth and interest in industrial applications of
machine learning (ML) in recent years. ML engineers, as a consequence, are in
high demand across the industry, yet improving the efficiency of ML engineers
remains a fundamental challenge. Automated machine learning (AutoML) has
emerged as a way to save time and effort on repetitive tasks in ML pipelines,
such as data pre-processing, feature engineering, model selection,
hyperparameter optimization, and prediction result analysis. In this paper, we
investigate the current state of AutoML tools aiming to automate these tasks.
We conduct various evaluations of the tools on many datasets, in different data
segments, to examine their performance, and compare their advantages and
disadvantages on different test cases.
