Puneet Kumar Yadav

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.