Automated Machine Learning (AutoML): The Path to Self-Evolving Models

Abstract

The increasing complexity of machine learning (ML) model design has created a growing demand for automation in the end-to-end ML pipeline. Automated Machine Learning (AutoML) has emerged as a revolutionary paradigm that democratizes ML development by automating tasks such as data preprocessing, feature selection, model selection, and hyperparameter optimization. AutoML bridges the gap between expert data scientists and domain practitioners by enabling the creation of high-performing models with minimal human intervention. This paper explores the evolution of AutoML as a pathway toward self-evolving models—systems capable of autonomously improving their performance through continuous feedback and adaptation. It examines the core techniques underlying AutoML, including Bayesian optimization, meta-learning, neural architecture search (NAS), and reinforcement learning-based tuning strategies. Furthermore, the paper discusses the integration of AutoML with emerging technologies such as cloud computing and federated learning, highlighting its potential in building scalable, adaptive, and privacy-aware AI systems. Finally, it identifies key challenges in interpretability, computational cost, and fairness, concluding with insights into how AutoML will shape the future of autonomous, self-optimizing artificial intelligence.