Project Management Best Practices for Implementing Machine Learning Solutions in Business Environments

Abstract

Implementing machine learning (ML) solutions in business environments requires careful project management to ensure successful outcomes. This paper explores project management best practices for ML implementation, focusing on key stages such as project initiation, planning, execution, monitoring, and closure. Drawing on industry research and case studies, the paper identifies critical success factors, challenges, and strategies for overcoming obstacles in ML projects. By adopting effective project management practices, organizations can enhance the efficiency, effectiveness, and sustainability of ML initiatives, driving business value and competitive advantage in an increasingly data-driven world.