Design a video recommendation system

Q: Design a video recommendation system

This is a ML System Design interview question from Reddit for Machine Learning Engineer roles. View the full question and solution on PracHub.

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Question

Scenario

You are designing an ML-driven video recommendation product (home feed + “up next”) for a consumer app.

The interviewer focuses heavily on infrastructure and logging/observability.

Requirements

Serve personalized recommendations with low latency.
Handle both:
- Home feed (rank a set of candidates)
- Next video (contextual/session-based)
Support rapid iteration (new models/features) and safe experimentation.

What to cover

High-level architecture : offline training pipeline, online serving, candidate generation + ranking.
Data & logging design :
- What events to log (impressions, clicks, watch time, skips, likes, shares, follows, dwell, scroll depth, etc.)
- How to uniquely identify an “impression” and join it to outcomes
- How to avoid common logging pitfalls (position bias, missing-not-at-random, duplicate events)
Feature pipelines :
- Batch vs streaming features
- Feature store (online/offline consistency)
Model training & evaluation :
- Labels and objectives (CTR, watch time, completion, satisfaction)
- Offline metrics and online A/B testing
Serving infra & reliability :
- Latency budget, caching, fallback behavior, graceful degradation
- Monitoring, alerting, model/data drift detection
Privacy & compliance considerations in logging and retention.

Design a video recommendation system

Scenario

Requirements

What to cover

Solution

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