There are a few concepts that are core to machine learning in X-Pack. Understanding these concepts from the outset will tremendously help ease the learning process.
Machine learning jobs contain the configuration information and metadata necessary to perform an analytics task. For a list of the properties associated with a job, see Job Resources.
As part of the configuration information that is associated with a job, detectors define the type of analysis that needs to be done. They also specify which fields to analyze. You can have more than one detector in a job, which is more efficient than running multiple jobs against the same data. For a list of the properties associated with detectors, see Detector Configuration Objects.
See Function Reference.
A machine learning node is a node that has
node.ml set to
which is the default behavior. If you set
false, the node can
service API requests but it cannot run jobs. If you want to use X-Pack machine learning
features, there must be at least one machine learning node in your cluster. For more
information about this setting, see X-Pack Settings.