Team Practicum: Build a Crop-Disease Classifier with a Field Partner
Overview
What this challenge is about.
Clean noisy field photos, train a MobileNet classifier in PyTorch, and export to ONNX. Finish with a verifiable certificate.
The scenario
The Nairobi startup (around 20 staff, post-seed) serves around 40,000 farmers and is fundraising on the strength of its data flywheel; a working v1 classifier unblocks the next round.
The Brief
What you'll do, and what you'll demonstrate.
Train, evaluate, and package a maize-disease classifier that an Android engineer can ship to farmers in the field.
Earning criteria — what you'll demonstrate
- Apply transfer learning on a small, noisy real-world dataset
- Evaluate generalization across regions, not just random splits
- Export PyTorch models to mobile-friendly formats and measure latency
- Hand off an ML artifact to a non-ML engineering teammate
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.
- Transfer Learning
Apply transfer learning to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch 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.
- Model Export
Apply model export to solve real industry problems and demonstrate production-level capability.
- Label Cleaning
Apply label cleaning to solve real industry problems and demonstrate production-level capability.
- Edge Deployment
Apply edge deployment 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 transfer-learning model on noisy field data, exporting it for mobile, and writing the handoff doc is exactly the end-to-end MLE work that climate-tech and edge-AI startups hire for.
This challenge sharpens
- transfer-learning
- pytorch
- model-export
Computer Vision Engineer
Real-world image classification with held-out-region evaluation and failure-mode galleries is the bread-and-butter of junior CV engineers at vertical AI startups.
This challenge sharpens
- transfer-learning
- model-evaluation
- edge-deployment
Applied AI Scientist
Diagnosing label noise and quantifying region-shift generalization is the kind of applied-science rigor that startups expect from their first applied-AI hire.
This challenge sharpens
- label-cleaning
- model-evaluation
- transfer-learning