Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools

TLDR

Machine learning has seen rapid industrial adoption, creating high demand for engineers whose productivity is limited by repetitive pipeline tasks, prompting the rise of AutoML to automate preprocessing, feature engineering, model selection, hyperparameter tuning, and result analysis. This study examines the current state of AutoML tools designed to automate ML pipeline tasks. The authors evaluate multiple AutoML tools across diverse datasets and data segments, assessing performance and contrasting strengths and weaknesses in various test scenarios.

Abstract

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.