Overview
What this challenge is about.
GPU Cost Dashboard for an AI Consulting Practice. Intermediate challenge in code. Writing production code that solves real engineering problems, earn a block...
The Brief
What you'll do, and what you'll demonstrate.
Ship a per-client GPU cost dashboard that flags unattributed spend and lets partners catch unprofitable engagements within a week.
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
- Reconcile billing data across multiple cloud providers
- Design a cost-attribution model that handles tagging gaps
- Build a partner-facing dashboard with action-oriented views
- Document a tagging convention engineering will actually adopt
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.
- Cloud Cost Attribution
Apply cloud cost attribution to solve real industry problems and demonstrate production-level capability.
- Etl Pipelines
Apply etl pipelines to solve real industry problems and demonstrate production-level capability.
- Data Modeling
Apply data modeling to solve real industry problems and demonstrate production-level capability.
- Dashboarding
Apply dashboarding to solve real industry problems and demonstrate production-level capability.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Documentation
Apply documentation 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:
Data Engineer
Multi-cloud billing reconciliation + dashboard is the kind of pragmatic data-engineering project that ships value in week one of a new job.
This challenge sharpens
- etl-pipelines
- data-modeling
- cloud-cost-attribution
MLOps Engineer
Cost attribution for GPU experiments is MLOps-adjacent work that keeps research budgets honest at any AI company.
This challenge sharpens
- cloud-cost-attribution
- etl-pipelines
- dashboarding
AI Product Manager
Designing partner-facing cost views with clear action triggers is the AI PM's craft of turning data into decisions.
This challenge sharpens
- dashboarding
- documentation
- data-modeling