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

Communications of the Association for Information Systems Volume 56 Paper in press 2025 Machine Learning Model Deployment and Management: A Handson Tutorial Varol O. Kayhan University of South Florida Tim C. Smith University of South Florida Donald J. Berndt University of South Florida Jorge del Cuadro Independent Consultant Sukumar Vinnakota University of South Florida See next page for additional authors Follow this and additional works at: https://aisel.aisnet.org/cais Recommended Citation Kayhan, V. O., Smith, T. C., Berndt, D. J., del Cuadro, J., Vinnakota, S., & Yenikapalli, G. (In press). Machine Learning Model Deployment and Management: A Hands-on Tutorial. Communications of the Association for Information Systems, 56, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol56/iss1/40 This material is brought to you by the AIS Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in Communications of the Association for Information Systems by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact . Machine Learning Model Deployment and Management: A Hands-on Tutorial Cover Page Footnote [*Note: Authors contributed equally to this work. Names are listed in alphabetical order.] Authors Varol O. Kayhan, Tim C. Smith, Donald J. Berndt, Jorge del Cuadro, Sukumar Vinnakota, and Gopi Chand Yenikapalli This article is available in Communications of the Association for Information Systems: https://aisel.aisnet.org/cais/ vol56/iss1/40 C ommunications of the A ssociation for I nformation S ystems Accepted Manuscript Machine Learning Model Deployment and Management: A Hands-on Tutorial Varol O. Kayhan Tim C. Smith School of Information Systems and Management Muma College of Business University of South Florida 0000-0003-4453-1738 School of Information Systems and Management Muma College of Business University of South Florida 0009-0001-5388-9283 Donald J. Berndt Jorge del Cuadro* School of Information Systems and Management Muma College of Business University of South Florida 0000-0002-4900-8244 Independent Consultant Sukumar Vinnakota* Gopi Chand Yenikapalli* School of Information Systems and Management Muma College of Business University of South Florida School of Information Systems and Management Muma College of Business University of South Florida Please cite this article as: Kayhan, V. O., Smith, T. C., Berndt, D. J., del Cuadro, J., Vinnakota, S., & Yenikapalli, G. C. (in press). Machine Learning Model Deployment and Management: A Hands-on Tutorial. Communications of the Association for Information Systems. This is a PDF file of an unedited manuscript that has been accepted for publication in the Communications of the Association for Information Systems. We are providing this early version of the manuscript to allow for expedited dissemination to interested readers. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered, which could affect the content. All legal disclaimers that apply to the Communications of the Association for Information Systems pertain. For a definitive version of this work, please check for its appearance online at http://aisel.aisnet.org/cais/. Accepted Manuscript C ommunications of the A ssociation for I nformation S ystems Tutorial Paper ISSN: 1529-3181 Machine Learning Model Deployment and Management: A Hands-on Tutorial Varol O. Kayhan Tim C. Smith School of Information Systems and Management Muma College of Business University of South Florida 0000-0003-4453-1738 School of Information Systems and Management Muma College of Business University of South Florida 0009-0001-5388-9283 Donald J. Berndt Jorge del Cuadro* School of Information Systems and Management Muma College of Business University of South Florida 0000-0002-4900-8244 Independent Consultant Sukumar Vinnakota* Gopi Chand Yenikapalli* School of Information Systems and Management Muma College of Business University of South Florida School of Information Systems and Management Muma College of Business University of South Florida 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 handson 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. Keywords: Hands-on Tutorial, Machine Learning, Model Deployment, Model Management. [Department statements, if appropriate, will be added by the editors. Teaching cases and panel reports will have a statement, which is also added by the editors.] [*Note: Authors contributed equally to this work. Names are listed in alphabetical order.] This manuscript underwent [editorial/peer] review. It was received xx/xx/20xx and was with the authors for XX months for XX revisions. [firstname lastname] served as Associate Editor.] or The Associate Editor chose to remain anonymous.] Accepted Manuscript Machine Learning Model Deployment and Management: A Hands-on Tutorial 1 Introduction Recent developments in artificial intelligence (AI) and machine learning (ML) are driving many organizations to integrate AI and ML into their processes to drive innovation, optimize operations, and create new revenue streams. According to a 2023 report, over 69% of organizations have placed AI and ML at the top of their strategic priorities and integrated some form of AI into their operations, representing a 15% increase from the previous year (Ranganathan, 2023). This growing interest values the global AI/ML market at $150.2 billion in 2023, which is projected to grow at a rate of 36.8% from 2023 to 2030 (Markets and Markets, n.d.). Despite the increasing interest in incorporating AI/ML into business operations, organizations encounter numerous challenges, particularly in deploying and managing ML models (Markowitz & Wiley, 2021). For instance, datasets and models usually undergo a phenomenon called “drift” due to changes in contexts over time. Consider a spam em (...truncated)