anchor.diff:  
   . Click **Create**.
   
  -This data set can now be analyzed in {ml} jobs in Kibana.
  +This data set can now be analyzed in {ml} jobs in {kib}.
   
   
   [[ml-gs-jobs]]
  
positive.code: 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 
  job is running. 
  //To explore the results, click **View Results**.
  //TBD: image::images/ml-gs-job1-results.jpg["The total-requests job is created"]
  
  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>>.
  
negative.code: [[ml-gs-data]]
  === Identifying Data for Analysis
  
  For the purposes of this tutorial, we provide sample data that you can play with
  and search in {es}. When you consider your own data, however, it's important to
  take a moment and think about where the {xpack} {ml} features will be most
  impactful.
  
  The first consideration is that it must be time series data. The {ml} features
  are designed to model and detect anomalies in time series data.
  
  The second consideration, especially when you are first learning to use {ml},
  is the importance of the data and how familiar you are with it. Ideally, it is
  information that contains key performance indicators (KPIs) for the health,
  security, or success of your business or system. It is information that you need
  to monitor and act on when anomalous behavior occurs. You might even have {kib}
  dashboards that you're already using to watch this data. The better you know the
  data, the quicker you will be able to create {ml} jobs that generate useful
  insights.
  
  The final consideration is where the data is located. This tutorial assumes that
  your data is stored in {es}. It guides you through the steps required to create
  a _data feed_ that passes data to a job. If your own data is outside of {es},
  analysis is still possible by using a post data API.
  
  IMPORTANT: If you want to create {ml} jobs in {kib}, you must use data feeds.
  That is to say, you must store your input data in {es}. When you create
  a job, you select an existing index pattern and {kib} configures the data feed
  for you under the covers.
  
  
  