Day 106 of 133

ML System Design framework (7 steps)

The reusable scaffold for every design round: clarify → metrics → data → model → infra → eval → edges.

DSA · NeetCode Sliding Window

  • Sliding Window MaximumDSA · Sliding Window

    Interview questions to prep

    1. Is this a fixed-size or variable-size window? Why does that fit this problem?
    2. What's the invariant inside the window, and how do you maintain it on shrink/expand?
    3. Why is the overall pass O(n) even though the inner loop looks like it could be O(n²)?

ML System Design · Framework

  • Interview questions to prep

    1. Walk me through your 7-step framework for any ML system design interview.
    2. How do you avoid running out of time on the model section?
  • Interview questions to prep

    1. What are the first five clarifying questions you ask in any ML system design interview?
    2. How do you confirm the business metric vs the ML metric without burning 10 minutes on it?
  • Interview questions to prep

    1. How do you map a business metric to an offline ML metric?
    2. Walk through three real cases where offline gains didn't translate online.
  • Interview questions to prep

    1. How would you map a vague product opportunity into data, labels, baseline, metrics, and launch criteria?
    2. When should you avoid ML and ship a heuristic or rules-based product first?
    3. What sources of ML technical debt matter most: hidden feedback loops, undeclared consumers, stale data, or correction cascades?
    4. How do you identify product surfaces where ML can add value without creating brittle dependencies?

References & further reading