Model Monitoring

What is model monitoring?

Deploying a model isn't the finish line — it's the start of the on-call shift. User requests, model behavior and system resources all drift day to day. Model monitoring gives a deployed model a "health dashboard" that keeps watching performance, latency and output quality, raising an alarm the moment something's off, so you can fix it before users notice.

What metrics does it watch?

Performance metrics
Accuracy, recall and other business metrics — the barometer of whether the model is answering right.
Latency and throughput
How long a response takes and how many requests it handles per second; slow means lost users.
Output quality
Off-topic answers, hallucinations, weird tone — these "soft" signals matter too.
Resources and cost
GPU utilization, memory, cost per request — so the model doesn't quietly burn money.

Why can't you just ship it and forget?

The world keeps changing
What users say and what the industry's data looks like both shift. Good today doesn't guarantee good tomorrow.
Failures are often silent
Most problems aren't a sudden crash — accuracy just slides down bit by bit, invisible without monitoring.

What happens after you monitor?

When a metric goes wrong, you can trigger alerts, roll back to an older version automatically, or even kick off retraining. Monitoring isn't "looking" — it's the start of the see → judge → act loop.

Bottom line: model monitoring gives your AI a health check that never powers off, so it shouts when something's wrong.