[DOCS] Add Data Visualizer to the ML Getting Started tutorial (elastic/x-pack-elasticsearch#3171)

* [DOCS] Refreshed ML screenshots

* [DOCS] Added screenshots for ML Data Visualizer

* [DOCS] Addressed feedback about data visualizer

* [DOCS] Fixed typo in ML tutorial

Original commit: elastic/x-pack-elasticsearch@2603536a93

docs/en/ml/getting-started-single.asciidoc

@@ -1,24 +1,8 @@
 [[ml-gs-jobs]]
 === Creating Single Metric Jobs
 
-Machine learning jobs contain the configuration information and metadata
+At this point in the tutorial, the goal is to detect anomalies in the
-necessary to perform an analytical task. They also contain the results of the
+total requests received by your applications and services. The sample data
-analytical task.
-
-[NOTE]
---
-This tutorial uses {kib} to create jobs and view results, but you can
-alternatively use APIs to accomplish most tasks.
-For API reference information, see {ref}/ml-apis.html[Machine Learning APIs].
-
-The {xpackml} features in {kib} use pop-ups. You must configure your
-web browser so that it does not block pop-up windows or create an
-exception for your {kib} URL.
---
-
-You can choose to create single metric, multi-metric, population, or advanced
-jobs in {kib}. At this point in the tutorial, the goal is to detect anomalies in
-the total requests received by your applications and services. The sample data
 contains a single key performance indicator(KPI) to track this, which is the total
 requests over time. It is therefore logical to start by creating a single metric
 job for this KPI.

docs/en/ml/getting-started-wizards.asciidoc

@@ -0,0 +1,99 @@
+[[ml-gs-wizards]]
+=== Creating Jobs in {kib}
+++++
+<titleabbrev>Creating Jobs</titleabbrev>
+++++
+
+Machine learning jobs contain the configuration information and metadata
+necessary to perform an analytical task. They also contain the results of the
+analytical task.
+
+[NOTE]
+--
+This tutorial uses {kib} to create jobs and view results, but you can
+alternatively use APIs to accomplish most tasks.
+For API reference information, see {ref}/ml-apis.html[Machine Learning APIs].
+
+The {xpackml} features in {kib} use pop-ups. You must configure your
+web browser so that it does not block pop-up windows or create an
+exception for your {kib} URL.
+--
+
+{kib} provides wizards that help you create typical {ml} jobs. For example, you
+can use wizards to create single metric, multi-metric, population, and advanced
+jobs.
+
+To see the job creation wizards:
+
+. Open {kib} in your web browser and log in. If you are running {kib} locally,
+go to `http://localhost:5601/`.
+
+. Click **Machine Learning** in the side navigation.
+
+. Click **Create new job**.
+
+. Click the `server-metrics*` index pattern.
+
+You can then choose from a list of job wizards. For example:
+
+[role="screenshot"]
+image::images/ml-create-job.jpg["Job creation wizards in {kib}"]
+
+If you are not certain which wizard to use, there is also a **Data Visualizer**
+that can help you explore the fields in your data.
+
+To learn more about the sample data:
+
+. Click **Data Visualizer**. +
++
+--
+[role="screenshot"]
+image::images/ml-data-visualizer.jpg["Data Visualizer in {kib}"]
+--
+
+. Select a time period that you're interested in exploring by using the time
+picker in the {kib} toolbar. Alternatively, click
+**Use full server-metrics* data** to view data over the full time range. In this
+sample data, the documents relate to March and April 2017.
+
+. Optional: Change the number of documents per shard that are used in the
+visualizations. There is a relatively small number of documents in the sample
+data, so you can choose a value of `all`. For larger data sets, keep in mind
+that using a large sample size increases query run times and increases the load
+on the cluster.
+
+[role="screenshot"]
+image::images/ml-data-metrics.jpg["Data Visualizer output for metrics in {kib}"]
+
+The fields in the indices are listed in two sections.  The first section contains
+the numeric ("metric") fields. The second section contains non-metric fields
+(such as `keyword`, `text`, `date`, `boolean`, `ip`, and `geo_point` data types).
+
+For metric fields, the **Data Visualizer** indicates how many documents contain
+the field in the selected time period. It also provides information about the
+minimum, median, and maximum values, the number of distinct values, and their
+distribution. You can use the distribution chart to get a better idea of how
+the values in the data are clustered. Alternatively, you can view the top values
+for metric fields. For example:
+
+[role="screenshot"]
+image::images/ml-data-topmetrics.jpg["Data Visualizer output for top values in {kib}"]
+
+For date fields, the **Data Visualizer** provides the earliest and latest field
+values and the number and percentage of documents that contain the field
+during the selected time period. For example:
+
+[role="screenshot"]
+image::images/ml-data-dates.jpg["Data Visualizer output for date fields in {kib}"]
+
+For keyword fields, the **Data Visualizer** provides the number of distinct
+values, a list of the top values, and the number and percentage of documents
+that contain the field during the selected time period. For example:
+
+[role="screenshot"]
+image::images/ml-data-keywords.jpg["Data Visualizer output for date fields in {kib}"]
+
+In this tutorial, you will create single and multi-metric jobs that use the
+`total`, `response`, `service`, and `host` fields. Though there is an option to
+create an advanced job directly from the **Data Visualizer**, we will use the
+single and multi-metric job creation wizards instead.

docs/en/ml/getting-started.asciidoc

@@ -75,6 +75,7 @@ significant changes to the system. You can alternatively assign the
 For more information, see <<built-in-roles>> and <<privileges-list-cluster>>.
 
 include::getting-started-data.asciidoc[]
+include::getting-started-wizards.asciidoc[]
 include::getting-started-single.asciidoc[]
 include::getting-started-multi.asciidoc[]
 include::getting-started-next.asciidoc[]