A General Recipe for Automated Machine Learning in Practice
This work addresses the problem of practical implementation for researchers and practitioners in AutoML, but it is incremental as it synthesizes existing ideas rather than introducing new methods.
The paper tackles the lack of practical guidance for designing Automated Machine Learning (AutoML) systems by proposing a frame of reference to distill fundamental concepts into a single design, based on a narrative review of existing approaches.
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.