Overview
What this challenge is about.
Containerized Model Inference on Kubernetes for a Fintech. Advanced challenge in code. Writing production code that solves real engineering problems, earn a ...
The Brief
What you'll do, and what you'll demonstrate.
Move credit-risk inference onto autoscaling Kubernetes with sub-200ms p95 latency at 10x current load and a runbook the on-call team can use.
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
- Containerize and deploy a model service to a managed Kubernetes cluster
- Configure horizontal pod autoscaling against a custom metric
- Conduct a realistic load test and interpret latency curves
- Write a runbook an on-call engineer can use under pressure
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Cloud Computing for Data and ML
Master · Data Engineering
Strong alignment
This challenge maps to Cloud Computing for Data and ML 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.
- Kubernetes
Apply kubernetes to solve real industry problems and demonstrate production-level capability.
- Containerization
Apply containerization to solve real industry problems and demonstrate production-level capability.
- Autoscaling
Apply autoscaling to solve real industry problems and demonstrate production-level capability.
- Load Testing
Apply load testing to solve real industry problems and demonstrate production-level capability.
- Model Serving
Apply model serving to solve real industry problems and demonstrate production-level capability.
- Monitoring Design
Apply monitoring design 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
Kubernetes-native model serving with autoscaling and an on-call runbook is the day-one work of an MLOps engineer at any fintech or large-AI company.
This challenge sharpens
- kubernetes
- model-serving
- monitoring-design
AI Engineer
Closing the loop from container to production inference under SLA is the AI-engineer skillset that ships product features.
This challenge sharpens
- containerization
- model-serving
- load-testing
AI Solutions Architect
Designing autoscaling inference platforms with cost + latency trade-offs documented is the core craft of an AI solutions architect.
This challenge sharpens
- kubernetes
- autoscaling
- monitoring-design