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ML Monitoring Suite

$39

Model monitoring dashboards, alert configurations, data quality checks, and performance tracking.

📁 8 files🏷 v1.0.0
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"
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