What Gets Measured Gets Improved: Monitoring Machine Learning Applications in Their Production Environments

Machine learning (ML) applications face many new, hardly predictable aspects in their production environments. Detecting new aspects in an ML production environment and understanding their impacts on the ML application is crucial if organizations are to ensure ML applications functionality. A monitoring entity is essential if one is to monitor ML applications in their production environments, to both continually minimize risks and improve ML application's performance. But existing monitoring approaches are struggling to deal with specifics that arise from ML applications. We aim at deriving monitoring practices and providing a holistic view over required steps in successful ML applications monitoring. Since there has been little research on this topic, we followed a qualitative research approach, i.e., we conducted an interview study combined with a multivocal literature review. Thus, we provide a theoretical framework of an ML-enabled agent in its production environment, five characteristics of ML applications' production environments and 17 monitoring practices - 14 practices arranged sequentially on a typical quality management cycle and three cross-sectional practices. To outline the ML specifics that arise in monitoring ML applications, we investigate the five ML production environment characteristics' influences on the ML monitoring practices.