Implementation Patterns and Production Architecture

Real-time Scoring Pipeline

Production unsupervised anomaly detection requires sub-100ms latency for real-time decisions. Deploy Isolation Forest models as lightweight services—scikit-learn models serialize efficiently and load in milliseconds. For autoencoders, export to ONNX format and serve via optimized inference engines. Both models should run on CPU for cost efficiency; GPU is rarely needed for single-record inference.

Architecture Pattern: Feature store → Real-time feature computation → Model inference → Score normalization → Threshold check → Action routing. Cache feature computations for repeated entities within time windows.

Score Calibration

Raw anomaly scores lack interpretability. Calibrate scores to probabilities using historical labeled data: for a score of X, what percentage were actually anomalous? This enables meaningful thresholds ("flag if >80% probability") and consistent interpretation across model versions. Recalibrate after each model update.

Ensemble Strategies

Combine multiple unsupervised methods: average scores from Isolation Forest and autoencoder, or use voting (flag if any model exceeds threshold). Ensembles reduce individual model weaknesses and provide redundancy. Weight models by recent precision performance on labeled validation sets.

Monitoring Essential: Track score distributions daily. Sudden shifts indicate data drift or model degradation. Alert on precision/recall changes measured against labeled samples.

Retraining Pipeline

Schedule automated retraining (weekly/monthly) to adapt to evolving normal patterns. Validate new models against holdout anomaly sets before promotion. Maintain model versioning with instant rollback capability. Shadow mode deployment compares new model scores against production before switching.