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Organizations are increasingly integrating artificial intelligence and machine learning (ML) to drive innovation, optimize processes, and create new revenue streams. However, deploying and managing ML models are complex tasks that pose significant challenges. Despite their importance, there is a notable gap in academia regarding the inclusion of these topics in business analytics or data science curricula. This tutorial aims to bridge this gap by providing a hands-on tutorial for deploying and managing ML models using an open-source platform. The tutorial focuses on tracking and versioning models, converting them into reproducible projects, and deploying and serving them for real-time predictions. It is designed for students and instructors in higher education, offering a step-by-step approach to model deployment and management. The tutorial has been successfully implemented in several graduate-level courses, receiving positive feedback for its practical application and comprehensive coverage of the post-modeling stages of the ML lifecycle.
