Article type :

1

Author :

Tucker Timothy, Victor Peter

Volume :

3

Issue :

1

Abstract :

Abstract Machine learning (ML) has emerged as a powerful tool for extracting insights from data and driving business analytics. However, implementing ML projects successfully requires more than just technical expertise; it demands strategic planning, effective project management, and agile methodologies. This paper explores the intersection of machine learning implementation and agile project management, providing insights into strategies for integrating ML into business analytics initiatives. Drawing on principles from agile methodologies and best practices in ML implementation, we highlight key considerations, challenges, and strategies for maximizing the value of ML projects in the context of business analytics. By elucidating effective implementation strategies, this paper aims to equip organizations with the knowledge and tools necessary to leverage ML effectively for business analytics and drive competitive advantage.