Detecting and Alerting on Drift
Last updated September 6, 2024
Detecting and Alerting on Data Drift with Evidently AI
Data drift can significantly impact the performance of your machine learning models. Early detection and timely responses are crucial to maintain model accuracy. Evidently AI provides a powerful framework for detecting data drift and setting up alerts to ensure you stay informed about any changes in your data.
Detecting and Alerting on Drift
- Run Drift Reports: Regularly run drift reports using Evidently AI to compare your reference data (baseline) with your current data. The reports visually highlight any significant changes in the data distribution, allowing you to quickly identify potential drift.
- Set Thresholds: Configure thresholds for specific metrics or features to trigger alerts when changes exceed a certain level. These thresholds can be customized based on your specific needs and the impact of drift on your model.
- Configure Alerts: Integrate Evidenty AI with your existing monitoring systems or alerting tools to receive notifications when drift is detected. This ensures that you're notified promptly and can take action before the drift negatively impacts your model's performance.
- Define Triggers: Define specific triggers that will generate alerts. For example:
- Investigate and Respond: Once an alert is triggered, investigate the cause of the drift. Analyze the changes in your data and determine if corrective actions are necessary. This might involve retraining your model on updated data, adjusting the model parameters, or exploring other mitigation strategies to adapt to the new data distribution.
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