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Day 64 of 133

Monitoring & drift (data / concept / label) + DSA 1-D DP

PSI / KS / JS for drift; closed-loop retraining; delayed labels.

DSA · NeetCode 1-D DP

  • Maximum Product SubarrayDSA · 1-D DP

    Interview questions to prep

    1. State the DP: define the state, the transition, and the base case explicitly.
    2. Top-down (memoized recursion) vs bottom-up (tabulation) — which is more natural here, and why?
    3. Can you space-optimize from O(n) to O(1)? Show the rolling-window trick.
  • Word BreakDSA · 1-D DP

    Interview questions to prep

    1. State the DP: define the state, the transition, and the base case explicitly.
    2. Top-down (memoized recursion) vs bottom-up (tabulation) — which is more natural here, and why?
    3. Can you space-optimize from O(n) to O(1)? Show the rolling-window trick.

MLOps · Monitoring & drift

  • Data drift, concept drift, label driftMLOpsEvidently

    Interview questions to prep

    1. Compare data drift vs concept drift vs label drift — give an example of each.
    2. Walk through three statistical tests you'd use to detect drift (PSI, KS, JS).
  • Monitoring stack: predictions, latency, errorsMLOpsArize

    Interview questions to prep

    1. What metrics do you monitor for a deployed model beyond accuracy?
    2. How would you slice your model monitoring dashboard so a regression in one cohort doesn't get masked?
  • Closed-loop monitoring & retraining triggersMLOpsEugene Yan

    Interview questions to prep

    1. How would you decide when to retrain — schedule vs trigger-based?
    2. How do you monitor when ground-truth labels arrive with delay?

References & further reading