Comparative Study of Automated Machine Learning Services on Cloud Platforms
Duckki Lee
Duckki Lee, Associate Professor, Department of Smart Software, Yonam Institute of Technology, Jinju-si (Gyeongsangnam-do), Korea Republic of.
Manuscript received on 31 January 2026 | First Revised Manuscript received on 08 February 2026 | Second Revised Manuscript received on 12 February 2026 | Manuscript Accepted on 15 February 2026 | Manuscript published on 28 February 2026 | PP: 23-28 | Volume-15 Issue-3, February 2026 | Retrieval Number: 100.1/ijeat.D474815040426 | DOI: 10.35940/ijeat.D4748.15030226
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Automated machine learning (AutoML) has emerged as a practical approach to facilitate the adoption of machine learning by automating model development tasks, including preprocessing, model selection, and hyperparameter optimisation, thereby reducing reliance on specialised expertise. Recently, major cloud providers have integrated AutoML into their platforms to offer end-to-end machine-learning pipelines as managed services. However, the practical implications of cloud-based AutoML systems, particularly their system and operational aspects, remain insufficiently explored. This paper presents an empirical, system-oriented analysis of AutoML services provided by Microsoft Azure, Amazon Web Services, and Google Cloud Platform. Using representative regression and binary classification tasks, the predictive performance, evaluation metrics, and feature-importance results produced by each platform are compared. The study also examines how platform-level design choices influence usability, reproducibility, and lifecycle management. The results demonstrate that cloud AutoML platforms deliver high-performing models that operate without manual intervention, whereas differences among providers primarily reflect architectural and operational abstractions rather than algorithmic limitations. These findings suggest that cloud AutoML should be understood as an integrated system that combines automated modelling and MLOps capabilities and offers a viable pathway toward production-ready machine learning under real-world constraints.
Keywords: AutoML, MlaaS, Cloud Computing, Performance Evaluation
Scope of the Article: Computer Science and Engineering