Comparative Study of Automated Machine Learning Services on Cloud Platforms

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.