Overview
What this challenge is about.
Fuse lexical and dense search for an e-commerce catalog with OpenSearch and Qdrant, then benchmark nDCG@10 lift to earn your verifiable certificate.
The scenario
The retailer (~5,000 stores across 95 countries, ~EUR 30 B revenue band, ~250 engineers) treats search relevance as a same-store-sales lever; a 1% conversion lift on the search-driven traffic is worth ~EUR 18 M annually.
The Brief
What you'll do, and what you'll demonstrate.
Design and prototype a hybrid (BM25 + dense vector) search that lifts nDCG@10 by at least 12% over BM25 on the retailer's 90-day search logs.
Earning criteria — what you'll demonstrate
- Combine lexical and dense retrieval into a single ranked list
- Apply multilingual embedding models to a real polyglot catalog
- Evaluate ranking quality with nDCG@k on click-derived labels
- Communicate an architecture trade-off (latency, cost, freshness) to non-search engineers
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.
- Hybrid Search
Apply hybrid search to solve real industry problems and demonstrate production-level capability.
- Embedding Models
Apply embedding models to solve real industry problems and demonstrate production-level capability.
- Bm25
Apply bm25 to solve real industry problems and demonstrate production-level capability.
- Reciprocal Rank Fusion
Apply reciprocal rank fusion to solve real industry problems and demonstrate production-level capability.
- Multilingual Retrieval
Apply multilingual retrieval to solve real industry problems and demonstrate production-level capability.
- Evaluation
Apply 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:
AI Solutions Architect
Designing a hybrid retrieval architecture that survives multi-language, multi-tenant, and freshness constraints is the bread-and-butter deliverable of an AI solutions architect at any commerce company.
This challenge sharpens
- hybrid-search
- reciprocal-rank-fusion
- multilingual-retrieval
Machine Learning Engineer
Operating BM25 + dense retrieval as a measured ranking system maps directly onto how MLEs ship search and ads ranking.
This challenge sharpens
- bm25
- embedding-models
- evaluation
Data Scientist
Building click-derived labels and reporting nDCG@k by slice is the kind of measurement work data scientists own in search and recommendation teams.
This challenge sharpens
- evaluation
- hybrid-search
- multilingual-retrieval