Monitor Model Drift
Skill Verified ActiveImplement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.
To proactively detect and alert on data and concept drift in production ML systems, preventing performance degradation and impact on business metrics through automated monitoring and reporting.
Features
- Implement comprehensive model drift monitoring using Evidently AI
- Detect data drift with statistical tests (PSI, KS)
- Detect concept drift via performance monitoring
- Set up automated alerting and reporting workflows
- Schedule monitoring jobs for continuous oversight
Use Cases
- When production models show unexplained performance degradation
- When new data distributions differ from training data
- When seasonal shifts affect input features
- When regulatory requirements mandate model monitoring
Non-Goals
- Automated model retraining based on drift detection
- Real-time monitoring of every production system without scheduling
- Deep analysis of root causes beyond drift identification
Installation
/plugin install agent-almanac@pjt222-agent-almanacQuality Score
VerifiedTrust Signals
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