anchor.diff:  * The _host
   ////
   
  -You can then use the Elasticsearch `bulk` API to load the data set. The
  +You can then use the {es} `bulk` API to load the data set. The
   `upload_server-metrics.sh` script runs commands similar to the following
   example, which loads the four JSON files:
   
  
positive.code: necessary to perform an analytical task. They also contain the results of the
  analytical task.
  
  NOTE: This tutorial uses Kibana to create jobs and view results, but you can
  alternatively use APIs to accomplish these tasks.
  For API reference information, see <<ml-apis>>.
  
  To work with jobs in Kibana:
  
  . Open Kibana in your web browser and log in. If you are running Kibana
  locally, go to `http://localhost:5601/`.
  
  . Click **Machine Learning** in the side navigation:
  image::images/ml-kibana.jpg["Job Management"]
  
  You can choose to create single metric, multi-metric, or advanced jobs in
  Kibana. In this 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 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.
  
negative.code: ==== Managing Data Feeds
  
  A data feed can be started and stopped multiple times throughout its lifecycle.
  If you want to retrieve more data from {es} and the data feed is
  stopped, you must restart it.
  
  For example, if you did not use the full data when you created the job, you can
  