ERD for Real-time Recommender Inference Monitoring

情境

A recommender system logs every inference request and recommendation, captures user feedback, snapshots features, and computes monitoring metrics with drift alerts.

這張圖裡有什麼

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Separate InferenceRequest, Recommendation, and FeedbackEvent to isolate serving, ranking, and ground truth

Track ModelVersion and FeatureSnapshot to correlate drift with deployment and input distribution

Use MonitoringMetric and DriftAlert to store computed KPIs and operational actions

Use this ERD when designing a data store for real-time ML inference monitoring, especially for recommender or ranking systems requiring feedback loops and drift detection.

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Recommender System Inference Monitoring ERD

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