83 lines
3.7 KiB
Plaintext
83 lines
3.7 KiB
Plaintext
[role="xpack"]
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[[ml-jobs]]
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== Creating machine learning jobs
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Machine learning jobs contain the configuration information and metadata
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necessary to perform an analytics task.
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{kib} provides the following wizards to make it easier to create jobs:
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[role="screenshot"]
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image::ml/images/ml-create-job.jpg[Create New Job]
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A _single metric job_ is a simple job that contains a single _detector_. A
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detector defines the type of analysis that will occur and which fields to
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analyze. In addition to limiting the number of detectors, the single metric job
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creation wizard omits many of the more advanced configuration options.
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A _multi-metric job_ can contain more than one detector, which is more efficient
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than running multiple jobs against the same data.
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A _population job_ detects activity that is unusual compared to the behavior of
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the population. For more information, see
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{stack-ov}/ml-configuring-pop.html[Performing population analysis].
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An _advanced job_ can contain multiple detectors and enables you to configure all
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job settings.
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{kib} can also recognize certain types of data and provide specialized wizards
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for that context. For example, if you
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<<add-sample-data,added the sample web log data set>>, the following wizard
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appears:
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[role="screenshot"]
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image::ml/images/ml-data-recognizer-sample.jpg[A screenshot of the {kib} sample data web log job creation wizard]
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TIP: Alternatively, after you load a sample data set on the {kib} home page, you can click *View data* > *ML jobs*. There are {ml} jobs for both the sample eCommerce orders data set and the sample web logs data set.
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If you use {filebeat-ref}/index.html[{filebeat}]
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to ship access logs from your
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http://nginx.org/[Nginx] and https://httpd.apache.org/[Apache] HTTP servers to
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{es} and store it using fields and datatypes from the
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{ecs-ref}/ecs-reference.html[Elastic Common Schema (ECS)], the following wizards
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appear:
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[role="screenshot"]
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image::ml/images/ml-data-recognizer-filebeat.jpg[A screenshot of the {filebeat} job creation wizards]
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If you use {auditbeat-ref}/index.html[{auditbeat}] to audit process
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activity on your systems, the following wizards appear:
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[role="screenshot"]
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image::ml/images/ml-data-recognizer-auditbeat.jpg[A screenshot of the {auditbeat} job creation wizards]
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Likewise, if you use the {metricbeat-ref}/metricbeat-module-system.html[{metricbeat} system module] to monitor your servers, the following
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wizards appear:
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[role="screenshot"]
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image::ml/images/ml-data-recognizer-metricbeat.jpg[A screenshot of the {metricbeat} job creation wizards]
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These wizards create {ml} jobs, dashboards, searches, and visualizations that
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are customized to help you analyze your {auditbeat}, {filebeat}, and
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{metricbeat} data.
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[NOTE]
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===============================
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If your data is located outside of {es}, you cannot use {kib} to create
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your jobs and you cannot use {dfeeds} to retrieve your data in real time.
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Machine learning analysis is still possible, however, by using APIs to
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create and manage jobs and post data to them. For more information, see
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{ref}/ml-apis.html[Machine Learning APIs].
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===============================
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////
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Ready to get some hands-on experience? See
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{stack-ov}/ml-getting-started.html[Getting Started with Machine Learning].
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The following video tutorials also demonstrate single metric, multi-metric, and
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advanced jobs:
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* https://www.elastic.co/videos/machine-learning-tutorial-creating-a-single-metric-job[Machine Learning for the Elastic Stack: Creating a single metric job]
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* https://www.elastic.co/videos/machine-learning-tutorial-creating-a-multi-metric-job[Machine Learning for the Elastic Stack: Creating a multi-metric job]
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* https://www.elastic.co/videos/machine-learning-lab-3-detect-outliers-in-a-population[Machine Learning for the Elastic Stack: Detect Outliers in a Population]
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//// |