The 15-Day AI/ML Interview Bootcamp
Day 14 — ML system design: from notebook to production, with a Spring Boot serving endpoint
The machine learning system design interview end to end — requirements, feature pipelines and feature stores, offline vs online evaluation, A/B tests, serving latency, monitoring, drift and retraining — on a recommender, then a Spring Boot endpoint that serves the Day 4 fraud model.
You demo the fraud model from Day 4 at the team meeting. AUC 0.97, a clean threshold sweep, a confusion matrix everyone can read. The product manager says the sentence you were hoping for: "Can we have this in checkout by Friday?" And a month after Friday you learn what the notebook never told you. The checkout page times out because a feature takes 300 ms to compute. The model that scored 0.97 offline flags half as many frauds online, because "payments in the last hour" meant one thing in your training table and another in the service. And the number that finally wakes you up at 2 a.m. isn't …
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