Examination of the role of auto ML in democratizing machine learning

Abstract

The democratization of Machine Learning (ML) has emerged as a pivotal paradigm shift, aiming to make ML tools and techniques accessible to a broader audience beyond data scientists and experts. This review paper delves into the transformative role of Automated Machine Learning (AutoML) in realizing this vision. AutoML, as an innovative approach, streamlines the ML pipeline, automating various stages such as data preprocessing, model selection, and hyperparameter tuning. By reducing the barriers to entry and mitigating the technical complexities associated with ML, AutoML holds the potential to democratize ML expertise. The paper commences with a comprehensive exploration of the foundational concepts underpinning AutoML, elucidating its mechanisms and methodologies. Through a meticulous analysis of recent advancements, the review delineates the evolution of AutoML frameworks and tools. Keywords such as