Two complementary tools keep the Ntuition Engine honest: maintenance windows (for planned disruption) and tuning rules (for individual signals that need custom handling).
Maintenance windows
Configured on Settings → Ntuition Engine. A window does two things at once:
- Training hygiene — datapoints inside the window are dropped before models train, so a patch-night CPU spike is never learned as "normal Tuesday behavior."
- Detection suppression — anomalies from matching hosts are suppressed while the window is active, so patch night doesn't light up the Analysis page either.
Two kinds:
- One-off — an explicit start and end timestamp. Use for migrations, load tests, failover drills.
- Weekly — days of the week, a start time (HH:MM), and a duration. Use for recurring backup and patch schedules.
Each window takes a host pattern (substring match; blank = all hosts), so "every Saturday 02:00–06:00 on backup-" is one rule.
Create windows before the disruption. The training-hygiene effect is retroactive only in the sense that excluded intervals stay excluded at every future retrain — data from chaos that ran without a window will influence baselines until it ages out of the training lookback.
Tuning rules
Managed inline from the Ntuition (Analysis) page — every anomaly row has a Tune action, and the Tuning Rules table lists what's in force. A rule matches a metric (or *) and host (or *) and applies one of:
- suppress — never flag this combination. For the test box that's supposed to spike.
- threshold — custom bounds: a min value, max value, and/or max acceptable deviation (σ) that override the learned baseline for this combination.
Each rule takes a free-text note — future-you will want to know why the rule exists.
Which tool when?
| Situation | Tool |
|---|---|
| Planned, time-bounded disruption | Maintenance window |
| Recurring scheduled jobs | Weekly maintenance window |
| One metric/host pair that's forever weird | Tuning rule (suppress) |
| A signal where the learned baseline is wrong | Tuning rule (threshold) |
| Everything is too noisy platform-wide | Neither — raise global sensitivity (see Tuning Ntuition Engine Sensitivity) |