[[ml-introduction]]
== Introduction

Machine learning in {xpack} automates the analysis of time-series data by
creating accurate baselines of normal behaviors in the data, and identifying
anomalous patterns in that data.

Driven by proprietary machine learning algorithms, anomalies related to
temporal deviations in values/counts/frequencies, statistical rarity, and unusual
behaviors for a member of a population are detected, scored and linked with
statistically significant influencers in the data.

Automated periodicity detection and quick adaptation to changing data ensure
that you don’t need to specify algorithms, models, or other data
science-related configurations in order to get the benefits of {ml}.
//image::graph-network.jpg["Graph network"]

[float]
=== Integration with the Elastic Stack

Machine learning is tightly integrated with the Elastic Stack.
Data is pulled from {es} for analysis and anomaly results are displayed in
{kb} dashboards.

[float]
[[ml-concepts]]
=== Basic Concepts

There are a few concepts that are core to {ml} in {xpack}.
Understanding these concepts from the outset will tremendously help ease the
learning process.

Jobs::
  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 <<ml-job-resource, Job Resources>>.

Data feeds::
  Jobs can analyze either a batch of data from a data store or a stream of data
  in real-time. The latter involves data that is retrieved from {es} and is
  referred to as a _data feed_.

Detectors::
  Part of the configuration information associated with a job, detectors define
  the type of analysis that needs to be done (for example, max, average, rare).
  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 stream. For a list of the properties associated with detectors, see
  <<ml-detectorconfig, Detector Configuration Objects>>.

Buckets::
  Part of the configuration information associated with a job, the _bucket span_
  defines the time interval across which the job analyzes. When setting the
  bucket span, take into account the granularity at which you want to analyze,
  the frequency of the input data, and the frequency at which alerting is required.








//[float]
//== Where to Go Next

//<<ml-getting-started, Getting Started>> :: Enable machine learning and start
//discovering anomalies in your data.

//[float]
//== Have Comments, Questions, or Feedback?

//Head over to our {forum}[Graph Discussion Forum] to share your experience, questions, and
//suggestions.