Design a system to monitor a real-time prediction service for feature drift
WHAT IT TESTS: production ML observability beyond accuracy checks. ANSWER OUTLINE: async feature logging, distribution comparison via PSI/KS against training baseline, and threshold-based anomaly alerts.
WHAT IT TESTS: your ability to design a low-latency, decoupled observability pipeline for production ML that handles unlabeled data. ANSWER OUTLINE: instrument the prediction service to emit feature vectors and metadata asynchronously to a stream; maintain a reference profile from training data; run continuous statistical tests like PSI, KS, or Wasserstein on sliding windows; alert when drift exceeds dynamic thresholds or z-scores.
Read the original → datadoghq.com
- #mlops
- #machine-learning
- #monitoring
- #system-design
- #feature-drift
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