Overview
What this challenge is about.
Build a Multi-Region Online Inference Service with SLAs. Advanced challenge in design. Designing real products under real constraints, earn a blockchain-veri...
The Brief
What you'll do, and what you'll demonstrate.
Design and prototype a multi-region, SLA-compliant online inference service with verified failover behavior.
This is not a design exercise. It is the work a product designer does between a brief and a shipped interface. That distinction matters to every hiring manager who has seen candidates redesign Spotify's homepage and none who have worked under real product 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
- Design an SLA-driven inference topology across regions
- Apply blue/green, canary, and shadow rollout patterns correctly
- Stand up production-grade observability for ML serving
- Defend a topology choice in writing to a platform architect
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Machine Learning Systems
Master · Ai Systems
Strong alignment
This challenge maps to Machine Learning Systems at the Master level. It sharpens the same practical skills your coursework expects — but in a real industry context with actual constraints and deliverables.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Inference Serving
Apply inference serving to solve real industry problems and demonstrate production-level capability.
- Multi Region Deployment
Apply multi region deployment to solve real industry problems and demonstrate production-level capability.
- Kubernetes
Apply kubernetes to solve real industry problems and demonstrate production-level capability.
- Observability
Apply observability to solve real industry problems and demonstrate production-level capability.
- Load Balancing
Apply load balancing to solve real industry problems and demonstrate production-level capability.
- Sla Engineering
Apply sla engineering 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:
AI Solutions Architect
Designing multi-region inference topologies against hard SLAs is exactly the work AI solutions architects own at fintech and enterprise customers.
This challenge sharpens
- multi-region-deployment
- inference-serving
- sla-engineering
MLOps Engineer
Standing up observability and rollout strategies for ML serving is MLOps day-job, and this challenge gives the student a deployment story to point at.
This challenge sharpens
- inference-serving
- observability
- kubernetes
Machine Learning Engineer
MLEs increasingly own serving topology in cross-functional pods; this challenge bridges modeling skills into the operational reality.
This challenge sharpens
- inference-serving
- load-balancing
- observability