A Real-Time Monitoring Framework for Deployed Machine Learning Models with Drift Detection and Performance Analytics

Key Points

• This research aims to develop a framework for real-time monitoring of machine learning models to detect drift and analyze performance.
• Implemented a monitoring framework for deployed machine learning models.
• Integrated drift detection mechanisms to assess model performance.
• Evaluated effectiveness through performance analytics.
• The framework successfully detected drift in model performance over time.
• Performance analytics indicated improved accuracy and responsiveness to data changes.
• User feedback suggested high satisfaction with the monitoring tools.