anchor.diff:  
   The sample data for this tutorial contains information about the requests that
   are received by various applications and services in a system. A system
  -administrator might use this type of information to track the the total
  -number of requests across all of the infrastructure. If the number of requests
  -increases or decreases unexpectedly, for example, this might be an indication
  -that there is a problem or that resources need to be redistributed. By using
  -the {xpack} {ml} features to model the behavior of this data, it is easier to
  -identify anomalies and take appropriate action.
  -
  -Download this sample data from: https://github.com/elastic/examples
  -//Download this data set by clicking here:
  -//See  https://download.elastic.co/demos/kibana/gettingstarted/shakespeare.json[shakespeare.json].
  +administrator might use this type of information to track the total number of
  +requests across all of the infrastructure. If the number of requests increases
  +or decreases unexpectedly, for example, this might be an indication that there
  +is a problem or that resources need to be redistributed. By using the {xpack}
  +{ml} features to model the behavior of this data, it is easier to identify
  +anomalies and take appropriate action.
  +
  +Download this sample data by clicking here:
  +https://download.elastic.co/demos/machine_learning/gettingstarted/server_metrics.tar.gz[server_metrics.tar.gz]
   
   Use the following commands to extract the files:
   
   [source,shell]
   ----------------------------------
  -tar xvf server_metrics.tar.gz
  +tar -zxvf server_metrics.tar.gz
   ----------------------------------
   
   Each document in the server-metrics data set has the following schema:
  
positive.code: job is running.
  
  TIP: The `create_single_metic.sh` script creates a similar job and data feed by
  using the {ml} APIs. For API reference information, see <<ml-apis>>.
  
  [[ml-gs-job1-manage]]
  === Managing Jobs
  
negative.code: whether or not it's _tokenized_, or broken up into separate words.
  
  The sample data includes an `upload_server-metrics.sh` script, which you can use
  to create the mappings and load the data set. You can download it by clicking
  here: https://download.elastic.co/demos/machine_learning/gettingstarted/upload_server-metrics.sh[upload_server-metrics.sh]
  Before you run it, however, you must edit the USERNAME and PASSWORD variables
  with your actual user ID and password.
  
  The script runs a command similar to the following example, which sets up a
  mapping for the data set:
  