Design a Visual Search Backend for a Boutique Luxury Marketplace
Overview
What this challenge is about.
Build a visual search backend for a luxury marketplace: choose an embedding model, index 80,000 items, and hit <200 ms recall@12. Get a verifiable certificate.
The scenario
The marketplace (around 70 staff, around EUR 90 million annual GMV) has seen text-search struggle on the buyer behavior that drives the highest-AOV transactions.
The Brief
What you'll do, and what you'll demonstrate.
Ship a visual-search backend with recall@12 above 0.7 on the labeled query set and p95 latency under 200 ms.
Earning criteria — what you'll demonstrate
- Select and evaluate vision embedding models for retrieval
- Operate a vector index at catalog scale
- Measure retrieval quality with recall@k and qualitative review
- Communicate retrieval-quality limits to a non-technical merchandising audience
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Deep Learning for Computer Vision
Master · Computer Vision
Strong alignment
This challenge maps to Deep Learning for 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.
- Visual Search
Apply visual search to solve real industry problems and demonstrate production-level capability.
- Embeddings
Apply embeddings to solve real industry problems and demonstrate production-level capability.
- Clip
Apply clip to solve real industry problems and demonstrate production-level capability.
- Vector Search
Apply vector search to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Retrieval Evaluation
Apply retrieval 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:
Machine Learning Engineer
Owning a visual-search backend end-to-end is a canonical MLE deliverable at marketplaces and e-commerce companies.
This challenge sharpens
- visual-search
- embeddings
- vector-search
Computer Vision Engineer
Tuning vision embeddings for catalog retrieval is increasingly a CV-engineer specialization at marketplaces.
This challenge sharpens
- embeddings
- clip
- retrieval-evaluation
AI Engineer
Standing up the retrieval API plus the metadata pre-filter and the merchandising brief is exactly the bundle AI engineers ship at small product teams.
This challenge sharpens
- visual-search
- clip
- retrieval-evaluation