An MLOps Solution Framework for Transitioning Machine Learning Models into eHealth Systems

Over the past few years, there has been a growing experimentation of machine learning (ML)-based technologies in the healthcare domain. However, mostrelated initiatives struggle to progress beyond the prototypical research stage and transition to clinical use. Although this problem affects the adoption of ML across all industries, it is largely exacerbated in the highly regulated medical domain. Lately, MLOps has emerged as a new discipline encompassing practices and tools to streamline the development and maintenance of ML-enabled systems. Rooted in software engineering and inspired by DevOps, it places great emphasis on the automation of ML pipelines and model lifecycle. In this paper, we present an MLOps-based solution framework designed to streamline the transition of experimental ML models to production-ready components for eHealth systems. Our approach is designed to support the reliable integration and clinical deployment of ML-enabled tools that can assist healthcare professionals. The solution framework is being developed and validated in the context of “DARE– Digital Lifelong Prevention”, an Italian research project aimed at leveraging the potential of data to improve health promotion and prevention throughout the life course.