Overview
What this challenge is about.
Build an MLOps Platform Slice for a Fintech Risk Team. Expert-level challenge in code. Writing production code that solves real engineering problems, earn a ...
The Brief
What you'll do, and what you'll demonstrate.
Ship a v0 MLOps platform slice (tracking, registry, training pipeline, monitoring) for a fintech risk team's next model deployment.
This is not a coding exercise. It is the work a software engineer does between a Jira ticket and a merged PR. That distinction matters to every hiring manager who has seen candidates solve LeetCode problems and none who have shipped production code under real constraints.
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
- Architect a small but real MLOps platform across multiple components
- Operate an experiment-tracking + model-registry workflow end-to-end
- Implement input-drift and accuracy-proxy monitoring
- Hand a platform off to a domain team with a usable runbook
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.
- Mlops Design
Apply mlops design to solve real industry problems and demonstrate production-level capability.
- Experiment Tracking
Apply experiment tracking to solve real industry problems and demonstrate production-level capability.
- Model Registry
Apply model registry to solve real industry problems and demonstrate production-level capability.
- Monitoring
Apply monitoring to solve real industry problems and demonstrate production-level capability.
- Ci Cd
Apply ci cd to solve real industry problems and demonstrate production-level capability.
- Team Collaboration
Apply team collaboration 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
Shipping a platform slice across tracking, registry, orchestration, and monitoring is the literal MLOps engineer job description.
This challenge sharpens
- mlops-design
- experiment-tracking
- monitoring
Machine Learning Engineer
Wiring a model into a registered, monitored pipeline is the MLE's daily craft on production systems.
This challenge sharpens
- model-registry
- ci-cd
- monitoring
AI Solutions Architect
Designing the platform across components and handing it off cleanly is the architect's contribution to a risk-team's quarterly OKR.
This challenge sharpens
- mlops-design
- team-collaboration
- experiment-tracking