Open Internet by MindsNet

Detecting Silent Failures in Deployed Machine Learning Models

Deployed machine learning models can produce confident but incorrect outputs due to silent failures, such as corrupted weights or faulty sensor inputs. This can lead to undetected errors in production environments. The challenge is to detect such failures without relying on watchdog mechanisms that only catch crashes.

Computing & Technology, Computer Science, Machine Learning