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What should you monitor for an ML model in production (beyond uptime)?

Monitoring an ML system is more than CPU and latency; the model can silently rot while the dashboard stays green. The signal is the four-layer taxonomy (operational, data, prediction, outcome) and using inputs as leading indicators because labels lag.

Updated Aug 2026 · Grounded in real Applied AI Engineer interview loops and written to a senior-engineer editorial bar.

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LEARN THE BACKGROUND

These lessons teach the material this question tests, in order and from the beginning.

Applied AI Engineering·ProductionSign in13mSeeing inside a system whose failures are silentOrdinary monitoring tells you the request succeeded, which here means almost nothing. This lesson covers what to record so a bad answer is explainable a week later, and the three alerts worth having on a system that never throws.Applied AI Engineering·ProductionSign in180mProject: harden your system for a bad dayThe capstone. Take the system you have built across three modules and make it operable: one stated constraint, a cache with a correct key, a real trust boundary, logs that explain a bad answer a week later, and a rehearsed answer for when the model API is down.

UP NEXT ON YOUR JOURNEY

Next in this trackYour model scores well offline but worse online, and you suspect training-serving skew. How do you find it?Next in this trackHow do you manage model versions and promote a model from staging to production safely?Next in this trackYour online features are stale, and predictions suffer for it. How do you guarantee feature freshness?

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