Elevating Data Science Maturity: Toward a Process Model that Harnesses MLOps

Abstract

Data Science (DS) uses advanced analytical methods, such as Machine Learning, to extract value from data to improve organizational performance. However, numerous DS projects fail due to the complexity and difficulty of handling various managerial and technical challenges. Because of shortcomings in existing DS methodologies, new standardized approaches for DS project management are needed that respect both the business and data perspectives. In this paper, the concept for a DS process model to address common problems in DS, including a low level of process maturity and a lack of reproducibility, is outlined. This artifact is developed using the Design Science Research methodology and relies on MLOps principles to support the development and operationalization of the analytical artifacts in DS projects.