ML
ML Interview Roadmap
← Study plan·System Design

Day 122 of 133

Cold-start strategies (recsys, search, ads) + DSA review

New users vs new items; content-based bridges; bandits.

DSA · NeetCode Greedy

  • Partition LabelsDSA · Greedy

    Interview questions to prep

    1. Prove the greedy choice — why is the locally-optimal pick safe globally? (Exchange argument or staying-ahead.)
    2. When does greedy fail on a similar-looking problem, and what would you reach for instead (DP, BFS)?
    3. Walk through edge cases that often break naive greedy: ties, negatives, single element.

ML System Design · Cross-cutting trade-offs

  • Latency vs accuracy: caching, distillation, cascadesML System DesignEugene Yan

    Interview questions to prep

    1. What levers do you pull when accuracy is great but latency misses the budget?
    2. Walk through where you'd add caching in a RAG + LLM pipeline to halve P99.
  • Multi-model routing & cascadesML System DesignAnyscale

    Interview questions to prep

    1. How would you design a cascade: cheap model first, expensive only when needed?
    2. What's the right verifier for the cheap model's output — and when does it dominate cost?
  • Cold-start strategies (recsys, search, ads)ML System DesignEugene Yan

    Interview questions to prep

    1. Walk through cold-start strategies for new users vs new items.
    2. Compare bandit-based exploration vs content-based bridges for cold start — when does each fit?
  • Privacy-preserving ML: federated, DP, on-deviceML System DesignGoogle

    Interview questions to prep

    1. When would you reach for federated learning vs differential privacy vs on-device inference?
    2. What's the accuracy cost of DP-SGD at typical ε values, and how do you decide if it's acceptable?

Specialization · RecSys foundations

  • Collaborative filtering & matrix factorizationML System DesignGoogle

    Interview questions to prep

    1. Walk through matrix factorization for recommendations and how SGD trains it.
    2. What's the cold-start problem in CF and how do hybrid models fix it?
  • Content-based & hybrid recsysML System DesignGoogle

    Interview questions to prep

    1. Compare content-based vs collaborative filtering — when does each fail?
    2. How would you blend content and CF signals — what's the failure mode of naive averaging?
  • RecSys metrics: nDCG, MAP, Hit Rate, MRRML System DesignRead

    Interview questions to prep

    1. Compare nDCG, MAP, MRR, and Recall@k — when do you use each?
    2. Why do offline metrics often disagree with online A/B test results in recsys?

References & further reading