Towards Automated Machine Learning: Evaluation and Comparison of AutoML Approaches and Tools
This work addresses the challenge of improving efficiency for ML engineers in industrial applications by providing a comparative analysis of AutoML tools, but it is incremental as it focuses on evaluation rather than introducing new methods.
The paper investigates the current state of AutoML tools by evaluating and comparing their performance on various datasets to automate repetitive tasks in machine learning pipelines, such as data pre-processing and model selection, aiming to improve efficiency for ML engineers.
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.