Automated Machine Learning (AutoML): an overview of opportunities for application and research

The wider adoption of AI and machine learning (ML) applications has been limited by the high costs of infrastructure and scarcity of ML experts and data scientists. To address some of these concerns, automated ML (AutoML) systems have been developed alongside cloud computing platforms to mitigate so...

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Bibliographic Details
Published inJournal of information technology cases and applications Vol. 24; no. 2; pp. 75 - 85
Main Authors Singh, Vivek Kumar, Joshi, Kailash
Format Journal Article
LanguageEnglish
Published Routledge 03.04.2022
Subjects
Artificial Intelligence
Automated machine learning
AutoML
machine learning
predictive models
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Summary:The wider adoption of AI and machine learning (ML) applications has been limited by the high costs of infrastructure and scarcity of ML experts and data scientists. To address some of these concerns, automated ML (AutoML) systems have been developed alongside cloud computing platforms to mitigate some of the constraints in the wider adoption of ML technologies, including by small and medium size organizations. In this paper, we introduce AutoML, identify some of the fundamental steps in model development, and currently available operationalizations of these systems, before concluding with an outline of potential research opportunities for IS researchers in the field.
ISSN:1522-8053
2333-6897
DOI:10.1080/15228053.2022.2074585