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Model Monitoring Dashboard

$39

Drift detection, performance monitoring, and alerting for deployed models with Grafana dashboards and alerting rules.

📁 5 files🏷 v1.0.0
YAMLMarkdownJSONGrafana

📁 File Structure 5 files

model-monitoring-dashboard/ ├── LICENSE ├── README.md ├── alerts/ │ └── rules.yml ├── config.example.yaml └── dashboards/ └── main.json

📖 Documentation Preview README excerpt

Model Monitoring Dashboard

Drift detection, performance monitoring, and alerting for deployed models with Grafana dashboards and alerting rules.

Contents

  • alerts/rules.yml
  • config.example.yaml
  • dashboards/main.json

Quick Start

1. Extract the ZIP archive

2. Review the README and documentation

3. Customize configuration files for your environment

4. Follow the setup guide for your specific use case

Requirements

  • Python 3.10+ (for Python scripts)
  • Relevant CLI tools for your platform
  • Access to your target environment

License

MIT License — see LICENSE file.

Support

Questions or issues? Email megafolder122122@hotmail.com

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Part of [Ml Engineer](https://inity13.github.io/ml-engineer-toolkit/)

📄 Code Sample .yml preview

alerts/rules.yml groups: - name: model-monitoring-dashboard rules: - alert: HighErrorRate expr: rate(http_requests_total{status=~"5.."}[5m]) > 0.05 for: 5m labels: severity: critical annotations: summary: High error rate detected description: "Error rate is above 5% for 5 minutes" - alert: HighLatency expr: histogram_quantile(0.99, rate(http_request_duration_seconds_bucket[5m])) > 1.0 for: 10m labels: severity: warning annotations: summary: High P99 latency description: "P99 latency above 1s for 10 minutes" - alert: HighMemoryUsage expr: process_resident_memory_bytes / 1024 / 1024 > 512 for: 15m labels: severity: warning annotations: summary: High memory usage description: "Memory usage above 512MB for 15 minutes"
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