Monitoring and Logging

Monitoring ensures your model continues to perform in production:

• Metrics to track: API response time, error rates, prediction distribution.

   

• Data drift detection: Monitor changes in incoming data compared to training data.

   

• Logging: Capture inputs, outputs, and errors for debugging and auditing.

   

• Tools:

• Prometheus & Grafana for metrics.

   

• CloudWatch (AWS), Stackdriver (GCP), or Azure Monitor for cloud deployments.
  

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Example Scenario:

• A fraud detection model triggers alerts if unusual patterns in transactions are detected, enabling quick response.