What should you monitor for an ML model in production (beyond uptime)?
Monitoring an ML system goes past CPU and latency; the model can silently rot while the dashboard stays green. What matters is the four-layer taxonomy (operational, data, prediction, outcome) and using inputs as leading indicators because labels lag.
Updated Sep 2026 · Grounded in real GenAI, LLM, and AI/ML engineering interview loops and written to a senior-engineer editorial bar.
Monitoring an ML system goes past CPU and latency; the model can silently rot while the dashboard stays green. What matters is the four-layer taxonomy (operational, data, prediction, outcome) and using inputs as leading indicators because labels lag.
Lead with where the obvious approach breaks, because that is the judgment they are screening for — most candidates jump straight to the happy path and lose the room.
Then walk the failure back through the pipeline in order, naming the one metric the customer's exec sponsor actually cares about before you propose the fix.
