How can AI Automate End-to-End Data Science?

TL;DR

This paper surveys the field of Automated Data Science (AutoDS), discussing its challenges, existing approaches, and how AI can enable end-to-end automation to democratize data science for broader accessibility.

Contribution

It introduces the AutoDS challenge, proposes a comprehensive framework, categorizes existing literature, and explores AI's potential to fully automate data science workflows.

Findings

AutoDS aims to make data science accessible and scalable.

Existing approaches cover various problem setups and techniques.

AI has potential to automate end-to-end data science processes.

Abstract

Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the resulting quality heavily dependent on their experience and skills. To make data science more accessible and scalable, we need its democratization. Automated Data Science (AutoDS) is aimed towards that goal and is emerging as an important research and business topic. We introduce and define the AutoDS challenge, followed by a proposal of a general AutoDS framework that covers existing approaches but also provides guidance for the development of new methods. We categorize and review the existing literature from multiple aspects of the problem setup and employed techniques. Then we provide several views on how AI could succeed in automating end-to-end AutoDS. We hope this survey can serve as insightful guideline for the AutoDS field and provide inspiration for future research.