Lab Project: Compare Three Architectures on Your Own Mini-Benchmark
Overview
What this challenge is about.
Scope a mini-benchmark, implement three architectures, and run statistical tests to earn a verifiable certificate.
The scenario
The big-tech AI lab (anon, around 200 researchers) explicitly screens practicum applicants for benchmark hygiene — they have been burned by interns who claimed wins from single-seed runs.
The Brief
What you'll do, and what you'll demonstrate.
Design and run a fair three-architecture mini-benchmark with honest statistical reporting and a written lab report.
Earning criteria — what you'll demonstrate
- Design a fair benchmark across architecture families
- Apply statistical testing to ML results (no single-seed claims)
- Distinguish in-distribution from distribution-shift performance
- Write a publication-style lab report
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.
- Experiment Design
Apply experiment design to solve real industry problems and demonstrate production-level capability.
- Statistical Testing
Apply statistical testing to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Deep Learning
Design and train neural networks for complex pattern recognition tasks.
- Scientific Writing
Apply scientific writing 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 benchmarks and reporting wins with confidence intervals is the daily hygiene of a junior ML researcher, especially at labs that take reproducibility seriously.
This challenge sharpens
- experiment-design
- statistical-testing
- benchmarking
Research Scientist
Multi-seed runs, paired statistical tests, and workshop-style writing mirror the rigor expected from a research scientist's first ablation study.
This challenge sharpens
- statistical-testing
- scientific-writing
- experiment-design
Applied AI Scientist
The discipline of distribution-shift evaluation translates directly to applied AI work where deployment data never matches training data.
This challenge sharpens
- benchmarking
- pytorch
- deep-learning