Expand description
Production monitoring with drift detection Production monitoring with drift detection and model degradation alerts
This module provides comprehensive monitoring capabilities for transformation pipelines in production environments, including data drift detection, performance monitoring, and automated alerting.
Structs§
- ARIMA
Config - Configuration for ARIMA (AutoRegressive Integrated Moving Average) model
- Advanced
Anomaly Detector - Advanced anomaly detection system
- Alert
Config - Alert configuration
- Anomaly
Feedback - Feedback for anomaly detection tuning
- Anomaly
Insights - Anomaly insights summary
- Anomaly
Record - Anomaly record for historical analysis
- Anomaly
Thresholds - Anomaly detection thresholds
- Change
Point Config - Configuration for change point detection
- Drift
Detection Result - Data drift detection result
- Ensemble
Anomaly Detector - Ensemble anomaly detector combining multiple methods
- Forecast
Model - Time series forecasting model configuration
- Isolation
Forest Config - Configuration structures for various anomaly detection methods
- LOFConfig
- Configuration for Local Outlier Factor (LOF) detection
- MLAnomaly
Detector - Machine learning anomaly detector
- OneClassSVM
Config - Configuration for One-Class SVM anomaly detection
- Performance
Metrics - Performance degradation metrics
- Seasonal
Config - Configuration for seasonal time series decomposition
- Statistical
Detector - Statistical anomaly detector using multiple statistical methods
- Time
Series Anomaly Detector - Time series anomaly detector
- Time
Series Point - Time series data point
- Transformation
Monitor - Production monitoring system
Enums§
- Alert
Type - Alert types
- Anomaly
Severity - Anomaly severity levels
- Drift
Method - Drift detection methods
- Feedback
Type - Type of feedback for anomaly detection accuracy