anchor.diff:  . Click **Machine Learning** in the side navigation,
   then click **Create new job**.
   
  -. Click **Create single metric job**.
  +. Click **Create single metric job**. +
  ++
  +--
  +[role="screenshot"]
   image::images/ml-create-jobs.jpg["Create a new job"]
  +--
   
   . Click the `server-metrics` index. +
   +
   --
  +[role="screenshot"]
   image::images/ml-gs-index.jpg["Select an index"]
   --
   
  -. Configure the job by providing the following information:
  +. Configure the job by providing the following information: +
  ++
  +--
  +[role="screenshot"]
   image::images/ml-gs-single-job.jpg["Create a new job from the server-metrics index"]
  +--
   
   .. For the **Aggregation**, select `Sum`. This value specifies the analysis
   function that is used.
  
positive.code: picker in the {kib} toolbar. Since the sample data spans a period of time
  between March 23, 2017 and April 22, 2017, click **Absolute**. Set the start
  time to March 23, 2017 and the end time to April 1, 2017, for example. Once
  you've got the time range set up, click the **Go** button.
  image:images/ml-gs-job1-time.jpg["Setting the time range for the data feed"]
  +
  --
  A graph is generated, which represents the total number of requests over time.
  
negative.code: be unique in your cluster. You can also optionally provide a description of the
  job.
  
  . Click **Create Job**. +
  +
  --
  [role="screenshot"]
  image::images/ml-gs-job1.jpg["A graph of the total number of requests over time"]
  --
  
  As the job is created, the graph is updated to give a visual representation of
  the progress of {ml} as the data is processed. This view is only available whilst the
  