Overview
What this challenge is about.
Train a KG-augmented recommender on MovieLens data, compare recall and cold-start performance, and earn a verifiable certificate.
The scenario
The streaming startup (~70 staff, ~3M MAU in Brazil and Mexico) is preparing a Series B and the recommendation surface is the single feature that most affects retention.
The Brief
What you'll do, and what you'll demonstrate.
Decide whether a KG-augmented recommender beats a collaborative-filtering baseline enough to justify the engineering investment.
Earning criteria — what you'll demonstrate
- Compare collaborative-filtering and KG-augmented recommenders rigorously
- Evaluate cold-start performance, not just average performance
- Reason about the engineering cost of a KG layer in a recommender stack
- Communicate research results as a business go/no-go
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.
- Knowledge Graph Embeddings
Apply knowledge graph embeddings to solve real industry problems and demonstrate production-level capability.
- Recommender Systems
Apply recommender systems to solve real industry problems and demonstrate production-level capability.
- Benchmarking
Apply benchmarking to solve real industry problems and demonstrate production-level capability.
- Cold Start Evaluation
Apply cold start evaluation to solve real industry problems and demonstrate production-level capability.
- Pytorch
Apply pytorch to solve real industry problems and demonstrate production-level capability.
- Kg Augmented Ml
Apply kg augmented ml 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:
ML Researcher
Owning a controlled benchmark between two recommender families and writing the go/no-go is exactly the work ML researchers ship for product orgs.
This challenge sharpens
- knowledge-graph-embeddings
- recommender-systems
- benchmarking
Applied AI Scientist
Translating a research idea into a cost-aware business recommendation is the day-to-day of applied AI scientists at consumer-AI startups.
This challenge sharpens
- benchmarking
- kg-augmented-ml
- cold-start-evaluation
Machine Learning Engineer
Building the training and evaluation harness plus the engineering-cost estimate is the MLE skillset that recommender teams hire for.
This challenge sharpens
- pytorch
- recommender-systems
- benchmarking