Monitoring and Adapting ML Models on Mobile Devices

• URL: http://arxiv.org/abs/2305.07772v2
• Date: Wed, 17 May 2023 14:34:00 GMT
• Title: Monitoring and Adapting ML Models on Mobile Devices
• Authors: Wei Hao, Zixi Wang, Lauren Hong, Lingxiao Li, Nader Karayanni, Chengzhi Mao, Junfeng Yang, and Asaf Cidon
• Abstract summary: 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. We evaluate the system on two computer vision datasets, and show it consistently boosts accuracy compared to existing approaches.
• Score: 17.28565076128893
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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%.

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