Overview
What this challenge is about.
Define offline and online stores, build a materialization job, and run a parity test for 25 fraud features. Complete with a verifiable certificate.
The scenario
The fintech (around 350 staff, EU consumer-credit focus) is preparing a Series-C raise and treats ML-platform maturity as a diligence concern after the two skew incidents.
The Brief
What you'll do, and what you'll demonstrate.
Design and prototype a feature-store pattern that eliminates train/serve skew for the fraud model and shows a clear migration path for the other 17.
Earning criteria — what you'll demonstrate
- Distinguish offline vs online feature serving and the skew it causes
- Pick a feature-store shape (rolled-your-own vs Feast vs Tecton) with reasoning
- Implement a working materialization pipeline with parity tests
- Design a migration plan that respects existing model deadlines
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.
- Feature Store
Apply feature store to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering to solve real industry problems and demonstrate production-level capability.
- Airflow
Apply airflow to solve real industry problems and demonstrate production-level capability.
- Data Pipelines
Apply data pipelines to solve real industry problems and demonstrate production-level capability.
- Redis
Apply redis to solve real industry problems and demonstrate production-level capability.
- Parity Testing
Apply parity testing 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
Designing and shipping a feature-store v1 is the platform-MLOps work that every fintech and consumer-AI team eventually hires for after their first train/serve skew incident.
This challenge sharpens
- feature-store
- airflow
- parity-testing
Data Engineer
Owning the offline-to-online materialization pipeline and dbt + Airflow plumbing is core data-engineering work on any ML-platform team.
This challenge sharpens
- data-pipelines
- airflow
- feature-engineering
Machine Learning Engineer
MLEs increasingly own feature definitions end to end; this challenge bridges modeling fluency into the platform side that ships features other models reuse.
This challenge sharpens
- feature-engineering
- feature-store
- data-pipelines