ml-monitoring

What problem does it solve?

Production-grade ML monitoring addresses the lack of visibility into model performance, data drift, and observability in live deployments.

Core Features & Use Cases

• Drift detection, data quality monitoring, and alerting to prevent degraded models
• Observability dashboards and reports to support rapid root cause analysis
• Real-world scenario: detect data drift between training and production data and trigger alerts with remediation suggestions

Quick Start

Invoke the ml-monitoring skill to initialize drift detection on a deployed model and generate a drift report.