Automated Machine Learning: State-of-The-Art and Open Challenges

TL;DR

This paper surveys the current state of Automated Machine Learning (AutoML), focusing on techniques for automating model selection, hyper-parameter tuning, and other pipeline steps to reduce human effort and enable non-experts.

Contribution

It provides a comprehensive overview of recent AutoML methods, tools, frameworks, and discusses open challenges and future research directions in the field.

Findings

Survey of state-of-the-art AutoML techniques

Coverage of tools and frameworks for AutoML

Discussion of open challenges and future directions

Abstract

With the continuous and vast increase in the amount of data in our digital world, it has been acknowledged that the number of knowledgeable data scientists can not scale to address these challenges. Thus, there was a crucial need for automating the process of building good machine learning models. In the last few years, several techniques and frameworks have been introduced to tackle the challenge of automating the process of Combined Algorithm Selection and Hyper-parameter tuning (CASH) in the machine learning domain. The main aim of these techniques is to reduce the role of the human in the loop and fill the gap for non-expert machine learning users by playing the role of the domain expert. In this paper, we present a comprehensive survey for the state-of-the-art efforts in tackling the CASH problem. In addition, we highlight the research work of automating the other steps of the full complex machine learning pipeline (AutoML) from data understanding till model deployment. Furthermore, we provide comprehensive coverage for the various tools and frameworks that have been introduced in this domain. Finally, we discuss some of the research directions and open challenges that need to be addressed in order to achieve the vision and goals of the AutoML process.