10.1Why On-Device ML Needs Dedicated Testing

Applications increasingly run ML models directly on device (Core ML on iOS, TensorFlow Lite on Android) for features like image classification, text prediction, object detection, and face recognition. These models execute locally without network connectivity, providing faster inference and better privacy.

But on-device ML introduces testing challenges that server-side ML does not have. The model must load within memory constraints, inference must complete within latency budgets, and the model must produce acceptable accuracy on hardware with limited compute power. A model that works perfectly on a server with a GPU may perform poorly on a mid-range phone.