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Machine Learning Model Deployment and Management: A Hands-on Tutorial

Varol O. Kayhan et al.

Communications of the Association for Information Systems2025https://doi.org/10.17705/1cais.05639article
AJG 2ABDC A
Weight
0.41

Abstract

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.

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https://doi.org/https://doi.org/10.17705/1cais.05639

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@article{varol2025,
  title        = {{Machine Learning Model Deployment and Management: A Hands-on Tutorial}},
  author       = {Varol O. Kayhan et al.},
  journal      = {Communications of the Association for Information Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.17705/1cais.05639},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.