Abstract

Software organizations are increasingly incorporating machine learning (ML) into their product offerings, driving a need for new MLcentric data management tools. Such tools facilitate the initial development and deployment of ML applications, contributing to a crowded landscape of disconnected solutions targeted at different stages, or components, of the ML lifecycle. A lack of end-to-end ML pipeline visibility makes it hard to address any issues that may arise during a production deployment, such as unexpected output values or lower-quality predictions. In this paper, we introduce our prototype and our vision for MLTRACE, a platform-agnostic system that provides observability to ML practitioners.