Open access
Sep 2026
Scalable AutoML Frameworks for End-to-End Optimization of Data Science Workflows
This study proposes a scalable end-to-end AutoML framework that integrates automated preprocessing, feature engineering, model selection, hyperparameter optimization, and adaptive resource allocation within a unified optimization architecture to improve optimization efficiency while maintaining predictive robustness.
Devi Udariansyah, M. Alshar'e, Puneet Kumar Yadav et al.
· Journal of Data Science · 0 citations