AutoML and the Future of Machine Learning Development

Abstract

Automated Machine Learning (AutoML) has emerged as a transformative paradigm in the field of artificial intelligence, aiming to simplify and accelerate the process of developing machine learning (ML) models. By automating key stages such as feature engineering, model selection, and hyperparameter tuning, AutoML democratizes access to ML capabilities, enabling non-experts to build sophisticated models while freeing experts to focus on higher-level innovation. As machine learning continues to expand across industries, AutoML plays a central role in addressing challenges related to scalability, reproducibility, and efficiency. However, alongside its promise, AutoML introduces limitations concerning interpretability, customization, computational costs, and ethical considerations. This paper examines AutoML’s impact on the evolution of machine learning development, highlighting both its opportunities for democratization and acceleration, as well as the challenges that must be addressed for its sustainable adoption.