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.