Differences

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--- ai_model_drift_monitoring [2025/05/28 03:21] – [Example 3: Advanced Threshold Customization] eagleeyenebula
+++ ai_model_drift_monitoring [2025/05/28 03:23] (current) – [Best Practices] eagleeyenebula
@@ Line 196: / Line 196: @@
 Use visualization to provide additional context to detected drift.
-```python
+<code>
+python
 import matplotlib.pyplot as plt
 from ai_model_drift_monitoring import ModelDriftMonitoring
@@ Line 214: / Line 215: @@
     plt.legend()
     plt.show()
-```
+</code>
 **Explanation**:
-- Provides a visual representation of data distributions to verify drift and assess its impact.
+   * Provides a visual representation of data distributions to verify drift and assess its impact.
----
 ===== Extensibility =====
 . **Incorporate Statistical Methods**:
-   Extend the framework to use advanced statistical tests like Kolmogorov-Smirnov Test, Wasserstein Distance, or Chi-Square Test.
+   * Extend the framework to use advanced statistical tests like Kolmogorov-Smirnov Test, Wasserstein Distance, or Chi-Square Test.
 . **Multi-Dimensional Drift Detection**:
-   Expand from a one-dimensional comparison to multi-dimensional feature space drift analysis.
+   * Expand from a one-dimensional comparison to multi-dimensional feature space drift analysis.
 . **Logging Enhancements**:
-   Add structured logging (e.g., JSON logs) for integration with monitoring and alerting systems like Grafana or ELK.
+   * Add structured logging (e.g., JSON logs) for integration with monitoring and alerting systems like Grafana or ELK.
 . **Actionable Insights**:
-   Extend the alert system to trigger specific actions, such as retraining your model when drift is detected.
+   * Extend the alert system to trigger specific actions, such as retraining your model when drift is detected.
 . **Monitoring Pipelines**:
-   Integrate with data pipelines in tools like Apache Kafka or cloud platforms for large-scale drift monitoring.
+   * Integrate with data pipelines in tools like Apache Kafka or cloud platforms for large-scale drift monitoring.
----
 ===== 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**:
-  Adjust thresholds flexibly for different use cases, such as critical systems or lenient applications.
-- **Frequent Evaluation**:
-  Perform regular drift checks to avoid sudden model deterioration.
-- **Visualization**:
+**Dynamic Thresholding**:
-  Use visualization tools to complement automated drift detection alerts for better understanding.
+  * Adjust thresholds flexibly for different use cases, such as critical systems or lenient applications.
-- **Automation**:
+**Frequent Evaluation**:
-  Automate retraining or data validation when persistent drift is detected.
+  * Perform regular drift checks to avoid sudden model deterioration.
----
+**Visualization**:
+  * Use visualization tools to complement automated drift detection alerts for better understanding.
+**Automation**:
+  * Automate retraining or data validation when persistent drift is detected.
 ===== Conclusion =====