How far are we with automated machine learning? characterization and challenges of AutoML toolkits

Automated Machine Learning aka AutoML toolkits are low/no-code software that aim to democratize ML system application development by ensuring rapid prototyping of ML models and by enabling collaboration across different stakeholders in ML system design (e.g., domain experts, data scientists, etc.)....

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Bibliographic Details
Published in:	Empirical software engineering : an international journal Vol. 29; no. 4; p. 91
Main Authors:	Al Alamin, Md Abdullah, Uddin, Gias
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.07.2024 Springer Nature B.V
Subjects:	Algorithms Automation Cloud computing Collaboration Compilers Computer Science Dirichlet problem Empirical analysis Interpreters Machine learning Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE) Programming Languages Questions Rapid prototyping Software engineering Software Engineering/Programming and Operating Systems Subject specialists Systems design Toolkits Virtual communities AutoML ML system engineering Automated machine learning
ISSN:	1382-3256, 1573-7616
Online Access:	Get full text
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