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

Understand

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.