About This Architecture

ML model drift monitoring pipeline that establishes baseline distributions from training data, extracts feature embeddings and confidence scores, then continuously monitors production predictions against reference artifacts using PSI, KL divergence, and cosine distance metrics. The pipeline ingests incoming production images, extracts embeddings and predictions, and triggers drift computation every N requests to detect distribution shifts. Drift metrics including Population Stability Index, KL Divergence, and Embedding Cosine Distance are computed and persisted in SQLite for alerting and retraining decisions. This architecture ensures early detection of model degradation caused by data drift, concept drift, or feature distribution changes in production. Fork this diagram on Diagrams.so to customize drift thresholds, add alerting integrations, or adapt metric computation for your specific use case.

People also ask

How do I implement model drift monitoring in production to detect when my ML model's performance degrades due to data distribution changes?

This diagram shows a complete drift monitoring pipeline that generates baseline distributions and feature embeddings from training data, then continuously compares production predictions against these references using Population Stability Index (PSI), KL Divergence, and Embedding Cosine Distance metrics. Drift metrics are computed every N requests and stored in SQLite, enabling early detection of