Master Study AI

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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