Automated Machine Learning: Revolutionizing Data Science and Decision-Making

Abstract

Automated Machine Learning (AutoML) has emerged as a transformative force in data science, revolutionizing the way machine learning models are developed and deployed. This paper provides a comprehensive exploration of AutoML, tracing its evolution, methodologies, applications, challenges, and future prospects. With the exponential growth of data and the increasing demand for sophisticated predictive analytics, AutoML offers a promising solution by automating various stages of the machine learning pipeline, including model selection, hyperparameter optimization, and feature engineering Despite these hurdles, the potential of AutoML to accelerate innovation across diverse domains, from healthcare to finance to autonomous vehicles, is undeniable.