# Automated Machine Learning in Practice: State of the Art and Recent Results

## TL;DR

This paper reviews the current state of AutoML, emphasizing its practical application in industry and presenting recent benchmark results on key algorithms to demonstrate progress and challenges in automating machine learning model building.

## Contribution

It provides a comprehensive overview of AutoML's state of the art with a focus on practical use cases and recent benchmark comparisons of leading algorithms.

## Key findings

- AutoML enables more accessible machine learning model development.
- Recent benchmarks show significant progress in AutoML algorithm performance.
- AutoML's practical applicability is growing in industry contexts.

## Abstract

A main driver behind the digitization of industry and society is the belief that data-driven model building and decision making can contribute to higher degrees of automation and more informed decisions. Building such models from data often involves the application of some form of machine learning. Thus, there is an ever growing demand in work force with the necessary skill set to do so. This demand has given rise to a new research topic concerned with fitting machine learning models fully automatically - AutoML. This paper gives an overview of the state of the art in AutoML with a focus on practical applicability in a business context, and provides recent benchmark results on the most important AutoML algorithms.