83 lines
3.3 KiB
Plaintext
83 lines
3.3 KiB
Plaintext
[[ml-getting-started]]
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== Getting Started with Machine Learning
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++++
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<titleabbrev>Getting Started</titleabbrev>
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Ready to get some hands-on experience with the {xpackml} features? This
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tutorial shows you how to:
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* Load a sample data set into {es}
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* Create single and multi-metric {ml} jobs in {kib}
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* Use the results to identify possible anomalies in the data
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At the end of this tutorial, you should have a good idea of what {ml} is and
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will hopefully be inspired to use it to detect anomalies in your own data.
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You might also be interested in these video tutorials, which use the same sample
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data:
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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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[float]
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[[ml-gs-sysoverview]]
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=== System Overview
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To follow the steps in this tutorial, you will need the following
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components of the Elastic Stack:
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* {es} {version}, which stores the data and the analysis results
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* {xpack} {version}, which includes the {ml} features for both {es} and {kib}
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* {kib} {version}, which provides a helpful user interface for creating and
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viewing jobs +
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//ll {ml} features are available to use as an API, however this tutorial
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//will focus on using the {ml} tab in the {kib} UI.
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See the https://www.elastic.co/support/matrix[Elastic Support Matrix] for
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information about supported operating systems.
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See {stack-ref}/installing-elastic-stack.html[Installing the Elastic Stack] for
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information about installing each of the components.
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NOTE: To get started, you can install {es} and {kib} on a
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single VM or even on your laptop (requires 64-bit OS).
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As you add more data and your traffic grows,
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you'll want to replace the single {es} instance with a cluster.
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When you install {xpack} into {es} and {kib}, the {ml} features are
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enabled by default. If you have multiple nodes in your cluster, you can
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optionally dedicate nodes to specific purposes. If you want to control which
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nodes are _machine learning nodes_ or limit which nodes run resource-intensive
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activity related to jobs, see <<xpack-settings>>.
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[float]
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[[ml-gs-users]]
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==== Users, Roles, and Privileges
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The {xpackml} features implement cluster privileges and built-in roles to
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make it easier to control which users have authority to view and manage the jobs,
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{dfeeds}, and results.
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By default, you can perform all of the steps in this tutorial by using the
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built-in `elastic` super user. However, the password must be set before the user
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can do anything. For information about how to set that password, see
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<<security-getting-started>>.
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If you are performing these steps in a production environment, take extra care
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because `elastic` has the `superuser` role and you could inadvertently make
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significant changes to the system. You can alternatively assign the
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`machine_learning_admin` and `kibana_user` roles to a user ID of your choice.
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For more information, see <<built-in-roles>> and <<privileges-list-cluster>>.
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include::getting-started-data.asciidoc[]
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include::getting-started-wizards.asciidoc[]
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include::getting-started-single.asciidoc[]
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include::getting-started-multi.asciidoc[]
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include::getting-started-forecast.asciidoc[]
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include::getting-started-next.asciidoc[]
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