75 lines
2.6 KiB
Plaintext
75 lines
2.6 KiB
Plaintext
[[ml-introduction]]
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== Introduction
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Machine learning in {xpack} automates the analysis of time-series data by
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creating accurate baselines of normal behaviors in the data, and identifying
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anomalous patterns in that data.
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Driven by proprietary machine learning algorithms, anomalies related to
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temporal deviations in values/counts/frequencies, statistical rarity, and unusual
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behaviors for a member of a population are detected, scored and linked with
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statistically significant influencers in the data.
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Automated periodicity detection and quick adaptation to changing data ensure
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that you don’t need to specify algorithms, models, or other data
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science-related configurations in order to get the benefits of {ml}.
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//image::graph-network.jpg["Graph network"]
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[float]
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=== Integration with the Elastic Stack
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Machine learning is tightly integrated with the Elastic Stack.
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Data is pulled from {es} for analysis and anomaly results are displayed in
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{kb} dashboards.
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[float]
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[[ml-concepts]]
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=== Basic Concepts
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There are a few concepts that are core to {ml} in {xpack}.
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Understanding these concepts from the outset will tremendously help ease the
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learning process.
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Jobs::
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Machine learning jobs contain the configuration information and metadata
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necessary to perform an analytics task. For a list of the properties associated
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with a job, see <<ml-job-resource, Job Resources>>.
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Data feeds::
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Jobs can analyze either a batch of data from a data store or a stream of data
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in real-time. The latter involves data that is retrieved from {es} and is
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referred to as a _data feed_.
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Detectors::
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Part of the configuration information associated with a job, detectors define
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the type of analysis that needs to be done (for example, max, average, rare).
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They also specify which fields to analyze. You can have more than one detector
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in a job, which is more efficient than running multiple jobs against the same
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data stream. For a list of the properties associated with detectors, see
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<<ml-detectorconfig, Detector Configuration Objects>>.
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Buckets::
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Part of the configuration information associated with a job, the _bucket span_
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defines the time interval across which the job analyzes. When setting the
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bucket span, take into account the granularity at which you want to analyze,
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the frequency of the input data, and the frequency at which alerting is required.
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//[float]
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//== Where to Go Next
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//<<ml-getting-started, Getting Started>> :: Enable machine learning and start
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//discovering anomalies in your data.
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//[float]
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//== Have Comments, Questions, or Feedback?
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//Head over to our {forum}[Graph Discussion Forum] to share your experience, questions, and
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//suggestions.
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