Augu 2025 Updates

The main changes involve updating machine learning model templates to include enhanced model monitoring capabilities. The system now generates baseline metrics for tracking model performance over time, including feature drift detection and prediction calibration monitoring. New configuration files are created to store these baseline measurements, and the scoring pipeline is updated to calculate and log monitoring metrics during model execution.

The model training process now saves additional artifacts like baseline calibration curves and feature statistics that will be used to detect when the model's performance degrades in production. The scoring configuration has been restructured to better organize model artifacts and preprocessing steps.

Overall, these changes add production-ready model monitoring functionality while cleaning up the project structure, making it easier to track model health and detect when models need retraining.