Build vs buy + MLOps consolidation + DSA 2-D DP

Interview questions to prep

1. State the DP recurrence on (i, j) and the three cases (insert, delete, replace).
2. Can you space-optimize to O(min(m, n))?

Interview questions to prep

1. State the 2-D DP: indices, recurrence, base case. What's the order of fill?
2. Can you reduce 2-D to 1-D by reusing rows or columns? Walk through the dependency direction.
3. Top-down with memoization vs bottom-up — which is easier to reason about, and which is faster in practice?

Interview questions to prep

1. Walk through your decision framework for build vs buy on an ML platform component.
2. What recurring failure modes have you seen when teams 'build' something that should have been 'bought'?

Interview questions to prep

1. What axes would you use to evaluate an LLM API vendor?
2. How do you de-risk vendor lock-in when you adopt a hosted ML service?

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).

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?

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?