Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working
Overview
What this challenge is about.
Capstone Lab: Diagnose Why a Production Model Quietly Stopped Working. Advanced challenge in analysis. Analyzing real datasets and building models that drive...
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.
This is not a data exercise. It is the work an analyst does when stakeholders need answers from messy data. That distinction matters to every hiring manager who has seen candidates describe statistical methods and none who have extracted insight from messy, real-world data.
When you finish, you will have something most graduates do not: a real-world deliverable, verified by Ewance, that you can show to a hiring manager and say "I did this. Here is the proof."
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