2023/08/29 by Hernan Ceferino Vazquez, Vazquez, Hernan Ceferino
Computer Science · #Artificial Intelligence (cs.AI) #Data Stream Mining Techniques #FOS: Computer and information sciences #Fuzzy Logic and Control Systems #Machine Learning (cs.LG) #Machine Learning and Data Classification #Software Engineering (cs.SE)
paper · pdf · doi:10.48550/arxiv.2308.15647
openalex publication_date 2023/08/29 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.