Cured, but complacent?
Just as fatigue from too many alerts dulls focus, over-reliance on ML can do the same, but in subtler ways. Once teams begin to trust the system implicitly, there’ s a danger of assuming it’ s always right, or that it’ s caught everything worth catching.
So, understand the impact, consider how subtle configuration drifts are typically uncovered. Often, these affect a handful of customer environments for weeks and don’ t trigger alerts because these changes occur gradually, and behaviours technically fall within the learned baseline of ML systems. Such issues usually only come to light when support engineers notice an uptick in unusual helpdesk queries and decide to investigate manually.
Incidents like these are reminders that ML tools are remarkable, but they aren’ t
Enter Machine Learning( ML)-based monitoring, and with it, the promise of clarity. omniscient. They depend on training data, contextual parameters and decision thresholds – all of which can age or become misaligned with evolving systems. If no one is watching the watchers, important details can slip through the cracks.
ML monitoring is a game-changer, but it’ s not set-and-forget
None of this is to say ML-based monitoring isn’ t valuable. When used well, it transforms how MSPs scale operations. ML-based alerting goes beyond static thresholds, drawing from telemetry, logs and real-time behavioural data to deliver a richer, more
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