Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working
Overview
What this challenge is about.
Diagnose a production model recall drop using fintech logs, run drift diagnostics, and propose fixes. Complete the postmortem to earn a verifiable certificate.
The scenario
The fintech AI team (around 12 engineers, part of a cross-border SME payments company in Singapore) runs around 40 fraud-related models in production and has a thin alerting story today.
The Brief
What you'll do, and what you'll demonstrate.
Diagnose a quiet production-model degradation, identify root cause from logs, and write the postmortem the team will actually act on.
Earning criteria — what you'll demonstrate
- Reproduce a production ML failure from logs alone
- Apply drift-detection statistics to real data
- Distinguish data drift, schema change, and concept drift in practice
- Write a postmortem that drives a real fix, not just blame
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Data Drift Detection
Apply data drift detection to solve real industry problems and demonstrate production-level capability.
- Model Monitoring
Apply model monitoring to solve real industry problems and demonstrate production-level capability.
- Root Cause Analysis
Apply root cause analysis to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Postmortem Writing
Apply postmortem writing to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
MLOps Engineer
Diagnosing a quiet production degradation, identifying the drift, and writing the postmortem is the bread-and-butter of MLOps work on the on-call side of an ML team.
This challenge sharpens
- data-drift-detection
- model-monitoring
- root-cause-analysis
Machine Learning Engineer
Reproducing failures from logs and proposing structural fixes is the MLE skill that separates engineers who keep models running from those who only ship v1s.
This challenge sharpens
- root-cause-analysis
- feature-engineering
- python
Data Scientist
Drift-detection statistics and chargeback-pipeline reasoning are core data-scientist skills for any team supporting production models.
This challenge sharpens
- data-drift-detection
- feature-engineering
- model-monitoring