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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.

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.

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