Overview
What this challenge is about.
Design a multi-region AI inference service with failover and SLA dashboards. Complete the challenge to earn a verifiable certificate.
The scenario
The fintech (around 4,000 staff, cross-border payments) recently breached its SLA in a quarterly review and now treats multi-region serving as a board-level concern.
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.
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