abstract = "With the explosion in the use of machine learning in
various domains, the need for an efficient pipeline for
the development of machine learning models has never
been more critical. However, the task of forming and
training models largely remains traditional with a
dependency on domain experts and time-consuming data
manipulation operations, which impedes the development
of machine learning models in both academia as well as
industry. This demand advocates the new research era
concerned with fitting machine learning models fully
automatically i.e., AutoML. Automated Machine
Learning(AutoML) is an end-to-end process that aims at
automating this model development pipeline without any
external assistance. First, we provide an insights of
AutoML. Second, we delve into the individual segments
in the AutoML pipeline and cover their approaches in
brief. We also provide a case study on the industrial
use and impact of AutoML with a focus on practical
applicability in a business context. At last, we
conclude with the open research issues, and future
research directions.",
notes = "Vishwakarma Government Engineering College, Gujarat
Technological University, Ahmedabad, India