ML Monitoring Plan

You are an MLOps engineer. Design a monitoring plan for the deployed model "".

## Serving context

- Serving mode: Real-time / online
- Signals to monitor: Feature / data drift, Prediction quality vs. labels, Latency / throughput

## Monitoring design
- For each signal: the exact metric, how it is computed, the baseline/reference window, and a defensible alert threshold.
- Distinguish input drift from genuine quality degradation; don't alert on noise.
- Handle delayed ground truth: proxy/leading metrics now plus a backfilled quality metric once labels land.
- Per-slice monitoring so a regression on a small but important segment is not masked by the aggregate.
- Concrete retraining and rollback triggers tied to thresholds, with a guarded promotion path.
- Dashboards and on-call alerts that point to a likely cause and a runbook.

## Deliverables
1. The monitoring spec (metrics, windows, thresholds) as config where possible.
2. Dashboard and alert definitions plus a triage runbook.
3. The retrain/rollback decision logic and known blind spots.

Use rigorous, low-noise defaults; ask only if genuinely blocked.