Metric Drift Detection & Management in AI Systems
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Course Modules:
Module 1: What Is Metric Drift?
Definition and real-world examples
Metric drift vs. data drift vs. concept drift
Why drift detection is critical in AI lifecycle
Module 2: Setting Up Performance Monitoring
Choosing metrics to monitor post-deployment
Real-time logging and batch evaluation
Establishing alert thresholds and baselines
Module 3: Detecting Drift in Classification & Regression
Monitoring accuracy, precision, recall, AUC over time
MSE, RMSE, and R² changes in regression models
Trend analysis and change-point detection
Module 4: Statistical Methods for Drift Detection
Kolmogorov–Smirnov test, Jensen–Shannon divergence
Population stability index (PSI)
Threshold setting and confidence intervals
Module 5: Responding to Metric Drift
Triggering model retraining
Updating thresholds and alert systems
Retraining vs. rebuilding: when and how to decide
Module 6: Capstone Project – Live Drift Monitoring Setup
Choose a use case (e.g., churn prediction, fraud detection)
Simulate a deployed model with streaming or batch inputs
Build a dashboard that visualizes drift and response strategy
Tools & Technologies Used:
Python, Scikit-learn
Evidently AI, MLflow, Prometheus
Google Colab / Jupyter Notebooks
Optional: Grafana, Streamlit, Airflow (for advanced users)
Target Audience:
Machine learning engineers and data scientists
MLOps professionals and AI product owners
Teams deploying AI systems in dynamic environments
QA and compliance teams monitoring AI reliability
Global Learning Benefits:
Detect and diagnose performance loss in AI models
Maintain AI system accuracy, fairness, and safety over time
Build scalable monitoring systems for enterprise AI use
Understand when and how to retrain or adjust deployed models
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