Auto ML for Optimizing Enterprise AI Pipelines: Challenges and Opportunities

Abstract

The increasing demand for artificial intelligence (AI) in enterprise applications has led to the development of automated machine learning (AutoML) systems aimed at streamlining the process of building, optimizing, and deploying AI models. This paper explores the challenges and opportunities in using AutoML to optimize enterprise AI pipelines. We begin by examining the core issues surrounding the integration of AutoML into complex enterprise environments, including data heterogeneity, model interpretability, scalability, and the need for domain expertise.