Differences

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--- ai_model_drift_monitoring [2025/05/28 03:22] – [Extensibility] eagleeyenebula
+++ ai_model_drift_monitoring [2025/05/28 03:23] (current) – [Best Practices] eagleeyenebula
@@ Line 237: / Line 237: @@
 ===== Best Practices =====
-- **Consistency in Data Collection**:
+**Consistency in Data Collection**:
-  Ensure that both reference and incoming data follow the same preprocessing and scaling procedures.
+  * Ensure that both reference and incoming data follow the same preprocessing and scaling procedures.
-- **Dynamic Thresholding**:
+**Dynamic Thresholding**:
-  Adjust thresholds flexibly for different use cases, such as critical systems or lenient applications.
+  * Adjust thresholds flexibly for different use cases, such as critical systems or lenient applications.
-- **Frequent Evaluation**:
+**Frequent Evaluation**:
-  Perform regular drift checks to avoid sudden model deterioration.
+  * Perform regular drift checks to avoid sudden model deterioration.
-- **Visualization**:
+**Visualization**:
-  Use visualization tools to complement automated drift detection alerts for better understanding.
+  * Use visualization tools to complement automated drift detection alerts for better understanding.
-- **Automation**:
-  Automate retraining or data validation when persistent drift is detected.
----
+**Automation**:
+  * Automate retraining or data validation when persistent drift is detected.
 ===== Conclusion =====