Design a system to detect training-serving skew for a numerical feature
Tests ML monitoring design via statistical distribution comparison between training and live data. Strong answers cover PSI/KS tests, windowed thresholding, and tiered alerting. Red flag: comparing raw values instead of distributions or ignoring alert fatigue.
This tests your ability to design production ML monitoring infrastructure that catches feature drift before it degrades model performance. A strong answer outlines statistical distribution comparisons using PSI or KS tests on sliding inference windows, defines dynamic thresholds calibrated to minimize false positives, and specifies tiered alerting with automated remediation such as model rollback or retraining triggers.
Read the original → docs.cloud.google.com
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