ML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)
ML System Design: Movie Recommendation Model and Pipeline
Design and sketch the implementation of a movie recommendation model and its data/training/serving pipeline for a streaming service. The system should, given a user, return a ranked list of movies they are likely to watch. Cover the full pipeline: ingesting interaction data, generating features/labels, training a recommendation model, evaluating it, and serving recommendations at low latency, including how to handle retraining and cold start.
Constraints & Assumptions:
- Catalog size: ~10^5 movies.
- User base: tens of millions of users.
- Primary interaction signal: watch events (explicit ratings, search, browse are optional).
- Online recommendations must return within a low-latency budget (tens of milliseconds) for a few hundred items.
- Model retraining cadence: regular (e.g., daily/weekly) on fresh interaction logs.
- New users and new movies appear continuously (cold start).
- AI coding tools are permitted; the interviewer expects critical review of AI-generated code.
AI interview prep
Core dilemma
Balancing the complexity of a sophisticated recommendation model and its training/serving pipeline against strict low-latency serving requirements and the need for continuous retraining and cold-start handling.
3 tradeoffs and 3 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
ML System Design: Movie Recommendation Model and Pipeline (AI-Assisted Round)
ML System Design: Movie Recommendation Model and Pipeline
Design and sketch the implementation of a movie recommendation model and its data/training/serving pipeline for a streaming service. The system should, given a user, return a ranked list of movies they are likely to watch. Cover the full pipeline: ingesting interaction data, generating features/labels, training a recommendation model, evaluating it, and serving recommendations at low latency, including how to handle retraining and cold start.
Constraints & Assumptions:
- Catalog size: ~10^5 movies.
- User base: tens of millions of users.
- Primary interaction signal: watch events (explicit ratings, search, browse are optional).
- Online recommendations must return within a low-latency budget (tens of milliseconds) for a few hundred items.
- Model retraining cadence: regular (e.g., daily/weekly) on fresh interaction logs.
- New users and new movies appear continuously (cold start).
- AI coding tools are permitted; the interviewer expects critical review of AI-generated code.