  ML models are increasingly being pushed to mobile devices, for low-latency
inference and offline operation. However, once the models are deployed, it is
hard for ML operators to track their accuracy, which can degrade unpredictably
(e.g., due to data drift). We design the first end-to-end system for
continuously monitoring and adapting models on mobile devices without requiring
feedback from users. Our key observation is that often model degradation is due
to a specific root cause, which may affect a large group of devices. Therefore,
once the system detects a consistent degradation across a large number of
devices, it employs a root cause analysis to determine the origin of the
problem and applies a cause-specific adaptation. We evaluate the system on two
computer vision datasets, and show it consistently boosts accuracy compared to
existing approaches. On a dataset containing photos collected from driving
cars, our system improves the accuracy on average by 15%.
