Adaptive Ensemble Detection with Hybrid Retraining (AEDHR)

1. 1Implement a tripartite ensemble of drift detectors using KS/PSI statistical tests, ADWIN error-rate monitoring, and performance baseline tracking
2. 2Combine the detectors with a majority-vote aggregation mechanism
3. 3Monitor data distributions in real-time to detect drift
4. 4Apply hybrid retraining to update models and maintain performance
5. 5Evaluate the effectiveness of AEDHR in managing data drift

Data scientists and machine learning engineers can benefit from this framework to improve the resilience of their models in dynamic production environments

💡 AEDHR framework combines real-time monitoring, intelligent decision-making, and sustainability-aware model maintenance to manage data drift

Improve ML model resilience with AEDHR!