Overview
What this challenge is about.
Quantize a Vision Model for a Smart-Doorbell SoC. 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.
Quantize a person-detection model to INT8 for a Cortex-A53 target without dropping accuracy below the product threshold, and decide whether QAT is needed.
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
- Apply post-training quantization to a real vision model
- Compare per-tensor vs. per-channel quantization schemes
- Benchmark inference on a real ARM target (or documented proxy)
- Reason about accuracy/latency/memory trade-offs for shipping
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.
- Quantization
Apply quantization 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.
- Onnx
Apply onnx 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.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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
Shipping a quantized vision model with honest on-device benchmarks is exactly the day-one work of an MLE at a consumer-IoT or edge-AI company.
This challenge sharpens
- quantization
- model-optimization
- edge-inference
MLOps Engineer
Reproducible export + calibration + benchmark scripts mirror the MLOps craft of building reliable model-shipping pipelines.
This challenge sharpens
- onnx
- benchmarking
- edge-inference
AI Engineer
Translating a model and a hardware constraint into a ship/no-ship recommendation is core AI-engineer work at any product-led AI startup.
This challenge sharpens
- quantization
- pytorch
- benchmarking