Machine Learning and Data Science Project Management From an Agile Perspective

Abstract

Successful implementations of machine learning (ML) and data science (DS) applications have enabled innovative business models and brought new opportunities for organizations. On the other hand, research studies report that organizations employing ML and DS solutions are at a high risk of failure and they can easily fall short of their objectives. One major factor is to adopt or tailor a project management method for the specific requirements of ML and DS applications. Therefore, agile project management (APM) may be proposed as a solution. However, there is significantly less study that explores ML and DS project management from an agile perspective. In this chapter, the authors discuss methods and challenges according to the background information and practice areas of ML, DS, and APM. This study can be viewed as an initial attempt to enhance these knowledge and practice domains in view of APM. Therefore, future research efforts will focus on the challenges as well as the experimental implementation of APM methods in real industrial case studies of ML and DS.