Phase 5: AI Impact Assessment & Governance · 40 min · Python · scikit-learn · matplotlib
Post-Deployment Monitoring — Drift, Bias, and Performance Tracking
Deployment is not the end. It's the start of monitoring.
Hiring signal: Post-deployment monitoring is where RAI engineering meets MLOps. Being able to design a monitoring system that tracks bias drift, performance drift, and data drift — with alerting thresholds — is a production skill that hiring managers test for.
What you will learn
- Describe the 3 types of drift: data drift, concept drift, and bias drift
- Build a monitoring module that detects drift using statistical tests
- Set alerting thresholds for fairness and performance metrics
- Produce a monitoring dashboard with drift visualizations
The Problem
A model that performs well at deployment can degrade over time. The world changes, data distributions shift, and bias can emerge in subgroups that weren't in the training data. Without monitoring, you won't know until a SEV-1 incident occurs. With monitoring, you detect drift early and respond before harm is done.
The 3 Types of Drift
| Drift Type | What Changes | Detection Method | Example |
|---|
| Data drift | Input feature distributions change | KS test, Population Stability Index (PSI) | Income distribution shifts due to economic change |
| Concept drift | The relationship between features and target changes | Model performance drop on recent data | After COVID, employment_years became less predictive of loan repayment |
| Bias drift | Fairness metrics degrade over time | Periodic bias audit on recent predictions | Disparate impact ratio drops from 0.80 to 0.65 over 3 months |
Unlock the full lesson
You've read the first 2 sections. The rest of this lesson covers Alerting Thresholds, Building the Monitoring Module, What's Next — plus a hands-on lab, quiz, and project artifact.
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