Automated Machine Learning: A Survey of Tools and Techniques

Junchi Tian; Chang Che

doi:10.70393/6a69656173.323336

OPEN ACCESS |Research Article ||1 December 2024

Automated Machine Learning: A Survey of Tools and Techniques

Junchi Tian

The George Washington University

junchi0905@gmail.com

The George Washington University, US.

Chang Che

The George Washington University

cche57@gwmail.gwu.edu

The George Washington University, US.

* Corresponding Author¹: Junchi Tian, E-Mail: junchi0905@gmail.com

Publication

Accepted Unknow ; Published 2024 December 1

Journal of Industrial Engineering and Applied Science, 2024, 2(6), 3005-6071.

Abstract

Automated Machine Learning (AutoML) has revolutionized the field of machine learning by automating complex and time-intensive tasks such as data preprocessing, model selection, and hyperparameter tuning. This study explores the capabilities, limitations, and practical applications of six widely used AutoML tools: Auto-sklearn, TPOT, H2O.ai, Google Cloud AutoML, Microsoft Azure AutoML, and Amazon SageMaker Autopilot. By evaluating these tools across diverse datasets—spanning tabular data, time series, image classification, and text sentiment analysis—the research highlights their predictive performance, computational efficiency, scalability, and explainability. Proprietary tools demonstrated superior scalability and efficiency through cloud integration, while open-source platforms provided more interpretability and flexibility. However, challenges such as lack of transparency in advanced neural architecture search mechanisms and ethical considerations, including bias mitigation, remain prevalent. This study concludes that while AutoML tools significantly lower the barrier to entry for machine learning, ongoing advancements are required to balance performance, usability, and ethical standards, making AutoML an integral solution for real-world applications.

Keywords

Machine Learning , AutoML , Computational Efficiency .

Metadata

DOI:

10.70393/6a69656173.323336

ARK:

ark:/40704/JIEAS.v2n6a08

Pages: 71-76

References: 22

Disciplines: Computer Science

Subjects: Machine Learning

Cite This Article

APA Style

Tian, J. & Che, C. (2024). Automated machine learning: a survey of tools and techniques. Journal of Industrial Engineering and Applied Science, 2(6), 71-76. https://doi.org/10.70393/6a69656173.323336

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

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