This question evaluates expertise in designing end-to-end ML-driven recommendation systems, including competencies in real-time feature engineering, candidate generation, model serving, business-rule integration, monitoring, experimentation, and operational trade-offs for live content.
Design a system for a live-commerce platform that surfaces trending livestreams to users.
Assume an ML model for scoring trendiness or relevance already exists and can be called as a service. Your task is to design the end-to-end ML system around that model.
Discuss:
Follow-up: How would you ensure that a stream shown in the trending feed is actually still live?