Atlas › 09 Mobile & Cross-Platform Testing › On-Device ML Model Testing☰ Read as one page
On-Device ML Model Testing
10.1Why On-Device ML Needs Dedicated TestingApplications increasingly run ML models directly on device (Core ML on iOS, TensorFlow Lite on Android) for features like image…10.2What to Test for On-Device ML10.3Testing Inference Latency10.4Testing Model Accuracy on DeviceThe same model may produce slightly different results on different hardware due to floating-point precision differences between GPU, CPU…10.5Testing Fallback BehaviorWhen the on-device model fails to load (corrupted file, unsupported device, insufficient memory), the app must degrade gracefully to a…10.6Testing OTA Model Updates10.7Memory and Battery Impact TestingOn-device ML testing requires a blend of traditional functional testing (does it produce the right output?) and performance testing (does…