Overview
What this challenge is about.
Build a hybrid music recommender for an EU streaming app, evaluate with NDCG@10, and earn a verifiable certificate.
The scenario
The streaming app (around 130 staff, EUR 25M Annual Recurring Revenue) loses around 35 percent of new users within 14 days; recommender quality on cold-start is the single most contested metric in product-engineering planning.
The Brief
What you'll do, and what you'll demonstrate.
Decide whether a content-collaborative hybrid recommender beats the current collaborative baseline on cold-start users, with evidence the product team can act on.
Earning criteria — what you'll demonstrate
- Implement and tune matrix-factorization and hybrid recommenders
- Slice evaluation metrics by user segment to find where models actually differ
- Estimate sample sizes for an online A/B test before recommending one
- Trade off accuracy gains against latency and memory cost
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.
- Recommender Systems
Apply recommender systems to solve real industry problems and demonstrate production-level capability.
- Feature Engineering
Apply feature engineering 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.
- Python
Write clean, efficient Python for data processing, automation, and backend services.
- Ab Test Design
Apply ab test design 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
Recommender bake-offs with cold-start slices and an A/B recommendation are exactly what MLEs ship every quarter at consumer-AI companies.
This challenge sharpens
- recommender-systems
- model-evaluation
- ml-pipelines
Data Scientist
Sample-size estimation and slice-based evaluation are core data-science skills any product team values.
This challenge sharpens
- ab-test-design
- model-evaluation
- feature-engineering
Applied AI Scientist
Turning model comparison into a costed product recommendation is the applied AI scientist's daily output.
This challenge sharpens
- recommender-systems
- feature-engineering
- ab-test-design