From One Model To Many: Managing Dozens Of ML Models In The Enterprise

Abstract

Today, businesses depend on numerous models that are built and deployed using machine learning algorithms in their operations across various departments; however, managing these models in a scalable manner introduces a host of issues related to consistency, governance, and efficiency in operations. By failing to implement a unified strategy in these environments, a business incurs diminished benefits from their machine learning applications due to a lack of clear models regarding responsibility or overlooked model decay in their operations. This review highlights integrated model operations solutions that help in managing a large number of models in a systematic manner using strategies that include integrated models in a unified framework that promotes a system or structure that favors scalability in their architecture model.