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#monitoring

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01 ·Machine Learning Basics·★ MEMBER·8 min read ML System Design — The 90% Outside the Model The accuracy you hit in a notebook is not a promise about production. Feature definitions, training-serving skew, monitoring that catches slow decay, and the retraining loop — the 90% that lives outside the model, laid out in the order you actually design it. 02 ·Time Series·★ MEMBER·PAPER·11 min read Time-Series Anomaly Detection — The Math Behind the Alerts An alert should fire on the gap between what you observed and what that moment predicted — not on the raw size of a number. Four rulers for measuring that gap (robust statistics, forecast residuals, subsequence distance, changepoints), built up from analogy to formula, then the two things that actually break in production: how you pick the threshold and how you evaluate.