Journal of Artificial Intelligence, Machine Learning and Soft Computing

ABSTRACT

Automated Machine Learning (AutoML) is transforming the field of artificial intelligence by automating traditionally manual and labor-intensive tasks such as feature engineering, model selection, hyperparameter tuning, and deployment. By reducing the need for deep technical expertise, AutoML democratizes AI, enabling businesses and researchers to develop robust models efficiently. This paper provides a comprehensive review of AutoML, including its key components, recent advances, challenges, and future directions. We discuss popular AutoML frameworks, evaluation metrics, and applications across various domains such as healthcare, finance, and autonomous systems. Additionally, we highlight ongoing research trends, including neural architecture search (NAS) and automated deep learning pipelines, and emphasize the importance of interpretability and fairness in automated systems.

KEYWORDS: AutoML, Automated Machine Learning, Neural Architecture Search, Hyperparameter Optimization, Machine Learning Pipelines, Model Selection, Feature Engineering