Overview
DriftWatch is a lightweight, self-hosted model monitoring solution designed for production ML systems. It provides comprehensive drift detection, latency tracking, and intelligent alerting to ensure model performance remains optimal over time.
Key Features
- Drift Detection: Automatic detection of data and prediction drift
- Latency Tracking: Monitor model inference latency and performance
- Intelligent Alerting: Smart alerts for anomalies and drift events
- Self-Hosted: Deploy on your own infrastructure for data privacy
- Lightweight: Minimal resource overhead for production systems
- Easy Integration: Simple integration with existing ML pipelines
Technical Implementation
Monitoring Components
- Drift Detector: Statistical methods for detecting distribution shifts
- Latency Monitor: Tracks inference time and performance metrics
- Alert Engine: Configurable alerting rules and notifications
- Metrics Collector: Efficient metrics collection and storage
- Dashboard: Real-time visualization of model health
Detection Methods
- Statistical drift detection (KL divergence, Kolmogorov-Smirnov test)
- Prediction drift monitoring
- Feature distribution tracking
- Performance metric degradation detection
Key Capabilities
- Real-time drift detection
- Latency and throughput monitoring
- Customizable alert thresholds
- Historical trend analysis
- Integration with monitoring systems
- Low-overhead monitoring
- Detailed event logging
Code Repository
Explore the implementation on GitHub:
git clone https://github.com/Kernel-ML/driftwatch.git
cd driftwatch
pip install -e .
driftwatch start --config config.yaml
Use Cases
- Production model monitoring
- Drift detection and alerting
- Performance degradation detection
- Model retraining triggers
- Compliance and audit logging
Future Enhancements
- Advanced drift detection algorithms
- Integration with popular monitoring platforms
- Custom metric support
- Real-time model retraining triggers
- Enhanced visualization and reporting
Technologies Used
PythonPrometheusGrafana