Video Action Recognition for a Retail Loss-Prevention Startup
Overview
What this challenge is about.
Video Action Recognition for a Retail Loss-Prevention Startup. Expert-level challenge in code. Writing production code that solves real engineering problems,...
The Brief
What you'll do, and what you'll demonstrate.
Train an action-recognition model that achieves at least 75 percent recall on suspicious actions while staying under 1 FP per 1,000 customer-minutes.
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
- Fine-tune a video action-recognition model on a domain-extended dataset
- Evaluate under a hard FP budget tied to product cost
- Diagnose action-confusion failure modes
- Communicate model boundaries to legal + product stakeholders
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Computer Vision
Master · Computer Vision
Strong alignment
This challenge maps to Computer Vision 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.
- Video Understanding
Apply video understanding to solve real industry problems and demonstrate production-level capability.
- Action Recognition
Apply action recognition to solve real industry problems and demonstrate production-level capability.
- 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.
- Ml Pipelines
Apply ml pipelines 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
Video action-recognition under a hard FP budget is the day-one MLE work at any retail-AI or surveillance-AI company.
This challenge sharpens
- video-understanding
- action-recognition
- ml-pipelines
Computer Vision Engineer
Action recognition with temporal evaluation is the CV-engineer specialization video product teams hire for.
This challenge sharpens
- action-recognition
- video-understanding
- transfer-learning
AI Safety Researcher
Designing for an FP budget and addressing legal stakeholders explicitly is the safety-aware engineering AI safety researchers practice.
This challenge sharpens
- model-evaluation
- video-understanding
- action-recognition