Automated Machine Learning Pipeline Optimization: Analyzing techniques for automating the optimization of machine learning pipelines to enhance efficiency

Abstract

Automated machine learning (AutoML) has emerged as a powerful tool for democratizing machine learning by enabling non-experts to build high-performing models. However, the optimization of machine learning pipelines remains a complex and time-consuming task, often requiring expertise and manual intervention. This paper provides a comprehensive overview of techniques for automating the optimization of machine learning pipelines, focusing on enhancing efficiency and reducing the need for manual tuning. We discuss key concepts, challenges, and recent advancements in AutoML pipeline optimization. We also present a comparative analysis of popular AutoML tools and frameworks, highlighting their strengths and limitations. Finally, we discuss future research directions and the potential impact of automated pipeline optimization on the field of machine learning.