Overview
What this challenge is about.
Compile and benchmark ResNet-50 ONNX across TensorRT, ONNX Runtime, and OpenVINO. Report latency, throughput, and memory trade-offs for a verifiable certificate.
The scenario
The Stuttgart robotics company (around 180 staff, factory-floor humanoid pilot programs) ships software updates to its robot fleet biweekly and needs a compiler choice that scales across its hardware mix.
The Brief
What you'll do, and what you'll demonstrate.
Pick the best ML compiler per hardware target with a fair benchmark and a memo that defends the choices.
Earning criteria — what you'll demonstrate
- Understand the ML-compiler stack landscape end to end
- Run a fair cross-compiler benchmark on shared hardware
- Quantify the accuracy, latency, and memory trade-offs of compilation
- Recommend a compiler stack per hardware target with evidence
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.
- Ml Compilers
Apply ml compilers to solve real industry problems and demonstrate production-level capability.
- Tensorrt
Apply tensorrt to solve real industry problems and demonstrate production-level capability.
- Onnx
Apply onnx to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Hardware Targeting
Apply hardware targeting to solve real industry problems and demonstrate production-level capability.
- Model Evaluation
Apply model evaluation 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:
ML Researcher
Designing fair cross-compiler benchmarks and writing the trade-off memo is the kind of applied-systems research work that platform-research teams hire for.
This challenge sharpens
- ml-compilers
- benchmarking
- model-evaluation
Machine Learning Engineer
Owning a model's deployment path across multiple hardware targets via compilers is the work MLEs increasingly handle on robotics and edge teams.
This challenge sharpens
- tensorrt
- onnx
- hardware-targeting
MLOps Engineer
Standardizing a compiler stack across hardware targets and writing the per-stack quickstart is the platform-MLOps work that scales an ML team beyond ad-hoc deploys.
This challenge sharpens
- ml-compilers
- benchmarking
- hardware-targeting