Overview
What this challenge is about.
You will search a supernet to find a wearable ECG classifier under a 50 ms latency budget, then recommend one model in a memo. Earn a verifiable certificate.
The scenario
The startup (around 35 people, post-clinical-validation) ships about 4,000 patches per month into hospital and home-monitoring programs across Israel and Germany; arrhythmia recall on the rarest class (about 3% prevalence) is the metric that drives clinical adoption.
The Brief
What you'll do, and what you'll demonstrate.
Run a small hardware-aware NAS for a microcontroller ECG classifier and decide whether a full NAS investment is justified for the next product cycle.
Earning criteria — what you'll demonstrate
- Define a hardware-aware NAS search space conditioned on a latency budget
- Run and evaluate a small evolutionary/random NAS
- Use latency lookup tables to avoid full-device measurement during search
- Reason about the ROI of NAS at a startup's compute scale
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.
- Neural Architecture Search
Apply neural architecture search to solve real industry problems and demonstrate production-level capability.
- Hardware Aware Design
Apply hardware aware design to solve real industry problems and demonstrate production-level capability.
- Edge Inference
Apply edge inference to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply evaluation to solve real industry problems and demonstrate production-level capability.
- Model Optimization
Apply model optimization 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:
Applied AI Scientist
Running a small NAS pipeline and translating the results into a scale-up recommendation is exactly the day-one work of an applied AI scientist at a healthtech or edge-ML startup.
This challenge sharpens
- neural-architecture-search
- hardware-aware-design
- model-optimization
Machine Learning Engineer
Hardware-aware model design with latency budgets is the MLE craft of shipping ML where it actually has to run.
This challenge sharpens
- hardware-aware-design
- edge-inference
- model-optimization
ML Researcher
Designing the search space and the proxy-vs-true latency validation is the kind of methodology question ML researchers tackle in industry research teams.
This challenge sharpens
- neural-architecture-search
- evaluation
- pytorch