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[DOCS] Updates ML getting started (#2505)
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szabosteve authored and mergify[bot] committed Aug 8, 2023
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3 changes: 2 additions & 1 deletion docs/en/stack/ml/get-started/ml-getting-started.asciidoc
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Expand Up @@ -47,7 +47,8 @@ user that has authority to manage {anomaly-jobs}. See <<setup>>.

. {kibana-ref}/get-started.html#gs-get-data-into-kibana[Add the sample data sets that ship with {kib}].

.. From the {kib} home page, click *Add data*, then select *Sample data*.
.. From the {kib} home page, click *Try samle data*, then open the
*Other sample data sets* section.

.. Pick a data set. In this tutorial, you'll use the *Sample web logs*. While
you're here, feel free to click *Add data* on all of the available sample data
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4 changes: 2 additions & 2 deletions docs/en/stack/ml/get-started/ml-gs-forecasts.asciidoc
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Expand Up @@ -9,8 +9,8 @@ In addition to detecting anomalous behavior in your data, you can use the
To create a forecast in {kib}:

. View your job results (for example, for the `low_request_rate` job) in the
**Single Metric Viewer**. To find that view, follow the link in the **Actions**
column on the **Anomaly Detection** page.
**Single Metric Viewer**. To find that view, click the **View series** button in
the **Actions** column on the **Anomaly Detection** page.

. Click **Forecast**.
+
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4 changes: 2 additions & 2 deletions docs/en/stack/ml/get-started/ml-gs-jobs.asciidoc
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Expand Up @@ -41,8 +41,8 @@ For more information, see <<ml-ad-datafeeds>>, <<ml-buckets>>, and
****

If you want to see all of the configuration details for your jobs and {dfeeds},
you can do so on the *Machine Learning* > *Anomaly Detection* > *Job Management*
page. Alternatively, you can see the configuration files in
you can do so on the *Machine Learning* > *Anomaly Detection* > *Jobs* page.
Alternatively, you can see the configuration files in
https://github.com/elastic/kibana/tree/{branch}/x-pack/plugins/ml/server/models/data_recognizer/modules/sample_data_weblogs[GitHub
]. For the purposes of this tutorial, however, here's a quick overview of the
goal of each job:
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11 changes: 6 additions & 5 deletions docs/en/stack/ml/get-started/ml-gs-results.asciidoc
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Expand Up @@ -154,11 +154,11 @@ Let's start by looking at the `response_code_rates` job in the
. Select the *Anomaly Detection* tab in *{ml-app}* to see the list of your
{anomaly-jobs}.

. Click the grid icon in the *Actions* column for your `response_code_rates` job
to view its results in the **Anomaly Explorer**.
. Open the `response_code_rates` job in the Anomaly Explorer to view its results
by clicking the corresponding icon in the row of the job.

For this particular job, you can choose to see separate swim lanes for each client
IP or response code. For example:
For this particular job, you can choose to see separate swim lanes for each
client IP or response code. For example:

[role="screenshot"]
image::images/ml-gs-job2-explorer.jpg["Anomaly explorer for response_code_rates job"]
Expand Down Expand Up @@ -191,7 +191,8 @@ image::images/ml-gs-job2-explorer-anomaly.jpg["Anomaly charts for the response_c

You can see exact times when anomalies occurred. If there are multiple detectors
or metrics in the job, you can see which caught the anomaly. You can also switch
to viewing this time series in the **Single Metric Viewer**.
to viewing this time series in the **Single Metric Viewer** by clicking the
**View Series** button in the **Actions** menu.

Below the charts, there is a table that provides more information, such as the
typical and actual values and the influencers that contributed to the anomaly.
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17 changes: 10 additions & 7 deletions docs/en/stack/ml/get-started/ml-gs-visualizer.asciidoc
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Expand Up @@ -17,19 +17,22 @@ exception for your {kib} URL.

--

. Click *Machine Learning* in the side navigation.
. Click *Machine Learning* in the {kib} main menu.

. Select the *{data-viz}* tab.

. Click *Select index* and choose the `kibana_sample_data_logs` {data-source}.
. Click *Select data view* and choose the `kibana_sample_data_logs` {data-source}.

. Use the time filter to select a time period that you're interested in
exploring. Alternatively, click
*Use full kibana_sample_data_logs data* to view the full time range of data.

. Optional: Change the sample size, which is the number of documents per shard
that are used in the {data-viz}. There is a relatively small number of
documents in the {kib} sample data, so you can choose a value of `all`. For
*Use full data* to view the full time range of data.

. Optional: You can change the random sampling behavior, which affects the
number of documents per shard that are used in the {data-viz}. You can use
automatic random sampling that balances accuracy and speed, manual sampling
where you can chose a value for the sampling percentage, or you can turn the
feaure off to use the full data set. There is a relatively small number of
documents in the {kib} sample data, so you can turn random sampling off. For
larger data sets, keep in mind that using a large sample size increases query
run times and increases the load on the cluster.

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