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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.
Das et al. (Thu,) studied this question.