[DOCS] Update screenshots in ML tutorial (elastic/x-pack-elasticsearch#3492)
* [DOCS] Update screenshots in ML tutorial * [DOCS] Updated screenshots for ML results Original commit: elastic/x-pack-elasticsearch@d17bc61d62
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@ -76,7 +76,7 @@ wizard in {kib}, you can accomplish this split by using the
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--
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.. For the **Key Fields**, select `host`. Note that the `service` field
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.. For the **Key Fields (Influencers)**, select `host`. Note that the `service` field
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is also automatically selected because you used it to split the data. These key
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fields are also known as _influencers_.
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When you identify a field as an influencer, you are indicating that you think
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@ -108,21 +108,22 @@ overwhelming and there is a small overhead to the analysis.
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. Click **Use full server-metrics* data**. Two graphs are generated for each
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`service` value, which represent the high mean `response` values and
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sum `total` values over time.
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//TBD What is the use of the document count table?
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sum `total` values over time. For example:
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+
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--
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[role="screenshot"]
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image::images/ml-gs-job2-split.jpg["Kibana charts for data split by service"]
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--
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. Provide a name for the job, for example `response_requests_by_app`. The job
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name must be unique in your cluster. You can also optionally provide a
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description of the job.
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. Click **Create Job**. As the job is created, the graphs are updated to give a
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visual representation of the progress of {ml} as the data is processed. For
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example:
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+
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--
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[role="screenshot"]
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image::images/ml-gs-job2-results.jpg["Job results updating as data is processed"]
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--
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. Click **Create Job**.
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When the job is created, you can choose to view the results, continue the job in
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real-time, and create a watch. In this tutorial, we will proceed to view the
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results.
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TIP: The `create_multi_metic.sh` script creates a similar job and {dfeed} by
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using the {ml} APIs. You can download that script by clicking
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