Slide 2 of 4

Tackle trends and anomalies with machine learning

Get ahead of problems before users notice

Fixed thresholds have real limitations.

A metric rising for hours with no signal, then an alert fires only after the limit is crossed
LimitationHow it affects you
Reactive by design: Alert fires only after the threshold is crossed.Reacting to capacity issues instead of preventing them
Can’t express how one instance differs from the restOutliers hide in aggregate data
No trend projectionGuessing at capacity planning instead of forecasting it

With Grafana Cloud Machine Learning, you can catch what threshold alerting can’t:

  • An instance trending toward trouble
  • A single node quietly diverging from the rest of the cluster

Grafana Cloud machine learning offers forecasting and outlier detection.

Real results from real teams

  • MediaKind

    “We see (Grafana Machine Learning) as a very useful tool for intelligent anomaly detection, and it will certainly become one of the tools that our SREs will use to increase their productivity and reduce their daily toil.”

Script

Fixed thresholds are reactive. They alert only after a limit is crossed, and they can’t tell you when one member of a group is behaving differently from the rest. Grafana Cloud machine learning catches what they can’t, with forecasting and outlier detection. The next two slides cover each in turn.
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