A General Recipe for Automated Machine Learning in Practice

TL;DR

This paper provides a comprehensive framework for designing practical AutoML systems, synthesizing key concepts and addressing real-world implementation challenges to advance automated machine learning.

Contribution

It introduces a unifying reference framework for building general AutoML systems based on a narrative review of existing approaches.

Findings

Distilled fundamental concepts for AutoML system design

Identified key challenges in practical AutoML implementation

Proposed a reference framework to guide future AutoML development

Abstract

Automated Machine Learning (AutoML) is an area of research that focuses on developing methods to generate machine learning models automatically. The idea of being able to build machine learning models with very little human intervention represents a great opportunity for the practice of applied machine learning. However, there is very little information on how to design an AutoML system in practice. Most of the research focuses on the problems facing optimization algorithms and leaves out the details of how that would be done in practice. In this paper, we propose a frame of reference for building general AutoML systems. Through a narrative review of the main approaches in the area, our main idea is to distill the fundamental concepts in order to support them in a single design. Finally, we discuss some open problems related to the application of AutoML for future research.