Data & Analytics
Design a Recommendation Engine (Batch vs. Streaming pipelines)
A hybrid recommendation platform combining offline batch model training with a low-latency online serving path for real-time personalization.
Open and Simulate this Architecture in InfraDraftCore Architectural Components
Batch Feature Pipeline
Offline ETL producing training features from historical data.
Model Training Pipeline
Collaborative filtering or embedding models trained on a schedule.
Feature Store
Keeps online and offline features consistent between training and serving.
Candidate Generation Service
Narrows the full catalog down to a manageable candidate set per user.
Real-Time Ranking/Scoring Service
Scores and orders candidates within the request's latency budget.
Streaming Event Ingestion
Feeds fresh signals — clicks, purchases — into near-real-time updates.
A/B Testing & Experimentation Framework
Measures whether a new model actually improves outcomes.