Design trending livestream discovery

Q: Design trending livestream discovery

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.

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ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Question

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:

the product goal and success metrics
what events and features you would collect
how to generate candidate livestreams
how to combine business rules, real-time signals, and the model score
the online and offline architecture
latency, freshness, and scaling requirements
how to prevent stale, low-quality, spammy, or ended streams from being recommended
experimentation, monitoring, and failure handling

Follow-up: How would you ensure that a stream shown in the trending feed is actually still live?

Design trending livestream discovery

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Solution

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