netdata/health/health.d/ml.conf

# below are some examples of using the `anomaly-bit` option to define alerts based on anomaly 
# rates as opposed to raw metric values. You can read more about the anomaly-bit and Netdata's 
# native anomaly detection here: 
# https://learn.netdata.cloud/docs/configure/machine-learning#anomaly-bit---100--anomalous-0--normal

# examples below are commented, you would need to uncomment and adjust as desired to enable them.

# alert per dimension example
# if anomaly rate is between 5-20% then warning (pick your own threshold that works best via tial and error).
# if anomaly rate is above 20% then critical (pick your own threshold that works best via tial and error).
# template: ml_5min_cpu_dims
#       on: system.cpu
#       os: linux
#    hosts: *
#   lookup: average -5m anomaly-bit foreach *
#     calc: $this
#    units: %
#    every: 30s
#     warn: $this > (($status >= $WARNING)  ? (5) : (20))
#     crit: $this > (($status == $CRITICAL) ? (20) : (100))
#     info: rolling 5min anomaly rate for each system.cpu dimension

# alert per chart example
# if anomaly rate is between 5-20% then warning (pick your own threshold that works best via tial and error).
# if anomaly rate is above 20% then critical (pick your own threshold that works best via tial and error).
# template: ml_5min_cpu_chart
#       on: system.cpu
#       os: linux
#    hosts: *
#   lookup: average -5m anomaly-bit of *
#     calc: $this
#    units: %
#    every: 30s
#     warn: $this > (($status >= $WARNING)  ? (5) : (20))
#     crit: $this > (($status == $CRITICAL) ? (20) : (100))
#     info: rolling 5min anomaly rate for system.cpu chart