Train a Sequence Model for Wearable-Telemetry Sleep Staging at a Healthtech
Overview
What this challenge is about.
Train LSTM, CNN+GRU, and transformer models on wearable sleep data, then compare to a rule-based stager. You get a verifiable certificate.
The scenario
The healthtech (around 110 staff, around 250,000 wearable users, FDA-cleared product line) needs the next stager to fit in the existing on-device inference budget or upstream metrics will regress.
The Brief
What you'll do, and what you'll demonstrate.
Pick the best sequence architecture for on-device sleep staging by trading off per-stage F1 against parameter count and FLOPs.
Earning criteria — what you'll demonstrate
- Train and compare recurrent and attention-based sequence models
- Reason about on-device deployment constraints early
- Evaluate per-class performance on imbalanced multi-class tasks
- Communicate architecture choices to an engineering audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning
Master · Deep Learning
Strong alignment
This challenge maps to Deep Learning 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.
- Sequence Models
Apply sequence models to solve real industry problems and demonstrate production-level capability.
- Lstm
Apply lstm to solve real industry problems and demonstrate production-level capability.
- Transformers
Apply transformers 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.
- On Device Feasibility
Apply on device feasibility 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:
Machine Learning Engineer
Comparing sequence architectures with on-device feasibility analysis is exactly the work MLEs ship at consumer-wearable companies.
This challenge sharpens
- sequence-models
- lstm
- transformers
Applied AI Scientist
Translating PSG-grade evaluation into an engineering recommendation memo is applied-AI-scientist territory in regulated wearable products.
This challenge sharpens
- sequence-models
- evaluation
- on-device-feasibility
MLOps Engineer
On-device feasibility and quantization analysis are increasingly MLOps responsibilities at consumer-device companies.
This challenge sharpens
- on-device-feasibility
- pytorch
- evaluation