A General Recipe for Automated Machine Learning in Practice
Hernan Ceferino Vazquez
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.