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ML Monitoring Suite
Model monitoring dashboards, alert configurations, data quality checks, and performance tracking.
MarkdownYAMLJSONDockerGrafanaPrometheus
📁 File Structure 8 files
ml-monitoring-suite/
├── LICENSE
├── README.md
├── config.example.yaml
├── docs/
│ ├── checklists/
│ │ └── pre-deployment.md
│ ├── overview.md
│ └── patterns/
│ └── pattern-01-layered-monitoring.md
└── templates/
└── config.yaml
📖 Documentation Preview README excerpt
ML Monitoring Suite
Comprehensive model monitoring dashboards with alert configurations, data quality checks, and performance tracking. Keep your production models healthy with continuous observability.
What's Included
- Grafana dashboard templates for model monitoring
- Prometheus metric exporters for ML systems
- Alert configurations for drift, performance, and errors
- Data quality check pipelines
- Performance tracking and reporting templates
- SLA monitoring and compliance dashboards
- Incident response runbook templates
Quick Start
# 1. Copy the example config
cp config.example.yaml config.yaml
# 2. Start the monitoring stack
docker-compose -f templates/docker-compose.yaml up -d
# 3. Import Grafana dashboards
python scripts/import_dashboards.py
# 4. Verify metrics are flowing
curl http://localhost:9090/api/v1/targets
Prerequisites
- Docker and Docker Compose
- Prometheus 2.x
- Grafana 9.x+
- Python 3.9+ (for custom exporters)
Contents
ml-monitoring-suite/
config.example.yaml
docs/
overview.md
patterns/
pattern-01-*.md
checklists/
pre-deployment.md
templates/
config.yaml
Support
For questions or issues, contact: megafolder122122@hotmail.com
License
MIT License - Copyright 2026 Jesse Mikkola. See LICENSE for details.
📄 Code Sample .yaml preview
config.example.yaml
# ML Monitoring Suite - Example Configuration
# Copy this file to config.yaml and update values for your environment
monitoring:
prometheus:
host: "localhost"
port: 9090
scrape_interval: "15s"
grafana:
host: "localhost"
port: 3000
admin_user: "admin"
admin_password: "changeme"
metrics:
model_serving:
- "prediction_latency_seconds"
- "prediction_count_total"
- "prediction_error_count_total"
- "model_version_info"
data_quality:
- "feature_null_rate"
- "feature_mean"
- "feature_stddev"
drift:
- "feature_drift_psi"
- "prediction_drift_score"
alerts:
latency_p99_threshold_ms: 200
error_rate_threshold: 0.05
drift_psi_threshold: 0.2
null_rate_threshold: 0.10
notification:
slack_webhook: "" # Set your Slack webhook URL
email: ""
logging:
level: "INFO"