Monitoring discriminative ML models using Amazon SageMaker AI with MLflow

This article demonstrates how to monitor discriminative ML models for data and model drift using Amazon SageMaker AI, MLflow, and the open-source Evidently library.

• Data drift detects changes in statistical properties of input data; model drift measures accuracy degradation from shifting patterns
• Solution uses Evidently presets to calculate data drift and model quality metrics from batch transform or real-time endpoint predictions
• Monitoring results and reports are stored in MLflow for tracking, comparing, and visualizing model lifecycle across training and deployment
• Pipelines can be scheduled with Amazon EventBridge and trigger Amazon SNS alerts when drift is detected
• Supports both batch inference and real-time endpoint architectures with optional AWS Lambda integration

The solution enables cost-effective, customizable model monitoring integrated with MLOps workflows for maintaining prediction accuracy in production.