Library › Book 9 › On-Device ML Testing
On-Device ML Testing
10.1🔒Why On-Device ML Needs Dedicated TestingApplications increasingly run ML models directly on device (Core ML on iOS, TensorFlow Lite on Android). These models execute locally…
10.2🔒What to Test
10.3🔒Testing Inference Latency
10.4🔒Testing Fallback Behavior
10.5🔒Memory and Edge Case Testing1. On-device ML needs latency, accuracy, memory, and battery testing -- not just functional testing. 2. Fallback to server-side API when…
10.6🔒Career Translation- Designed on-device ML testing framework validating inference latency (p95 < 200ms), memory stability across 50+ inference cycles, and…
10.7🔒Q&AInterview Depth CheckPrompt: Your image classification model passes all accuracy benchmarks in the test environment but runs 3x slower on a Samsung Galaxy A14…