What drives MLOps adoption? An analysis using the TOE framework

ABSTRACT

MLOps is essential to streamline the machine learning (ML) development process, ensure ML models stay operational, and provide users with the desired value. MLOps enhances the auditability, dependability, repeatability, and quality of ML data, models, and systems. MLOps technologies tackle several operational difficulties in an ML process. This research used the TOE framework to identify drivers and challenges to adopting MLOps tool. Data were collected from 277 professionals from various industries and AI/ML-related job roles. The responses were analysed using a three-step approach – Data Profiling, Chi-square tests and Logistic regression (LR) model. The analysis uncovered that ML usage, performance drivers, and security drive MLOps adoption, whereas regulatory environment, organizational preparation, and ML infrastructure moderately influence it. The investigation shows that management/leadership needs to be aware of MLOps technologies' benefits. This study provides insights to AI/ML professionals, academics, researchers, and machine learning model users on MLOps adoption.

KEYWORDS:

• MLOps
• ML operations
• machine learning
• artificial intelligence
• ML monitoring
• ML obserability
• Information systems

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Sibanjan Debeeprasad Das

Sibanjan Debeeprasad Das is a PhD student at the Indian Institute of Management (IIM) Ranchi studying Information Systems and Business Analytics. He earned a Master of Information Technology degree with a concentration in Business Analytics from Singapore Management University. Sibanjan has a number of industry certifications, including OCA, OCP, ITIL V3, CSCMS, Six Sigma Green Belt, and PGC in Digital Marketing. He has been actively contributing to the community by publishing books and articles, participating in a variety of industry panel discussions, serving as a guest speaker for conferences and Faculty Development programmes at universities, and mentoring AI/ML professionals. In addition to his many contributions to various research projects, conferences and edited book chapters, he is the author of two AI practitioner books: Hands on Automated Machine Learning Using Python and Data Science Using Oracle Data Miner and Oracle R Enterprise. He served on the technical reviewer panel for IEEE International Conference on Advances in Smart, Secure, and Intelligent Computing.

Pradip Kumar Bala

Pradip Kumar Bala is professor in the area of Information Systems & Business Analytics at Indian Institute of Management (IIM) Ranchi. He received his B.Tech., M.Tech. and Ph.D. from Indian Institute of Technology (IIT) Kharagpur in 1993, 1999 and 2009 respectively. He worked in Tata Steel before joining academics. He also worked as associate professor at Xavier Institute of Management Bhubaneswar and as assistant professor at IIT Roorkee before joining IIM Ranchi in 2012. His teaching and research areas include text mining & NLP, recommender systems, data mining applications, data mining and NLP algorithms, social media analytics and marketing analytics. He has conducted many training programmes in business analytics & business intelligence. He has published more than 100 research papers in reputed international journals, conference proceedings and book chapters. He is also a member of the International Association of Engineers (IAENG). He has served as Director In-charge, Dean (Academics), Chairperson, Post-Graduate Programmes, Chairperson, Doctoral Programme & Research, and Member of the Board of Governors of IIM Ranchi.