Getting started with machine learningedit

Ready to take machine learning for a test drive? Follow this tutorial to:

  • Try out the Data Visualizer
  • Create anomaly detection jobs for the Kibana sample data
  • Use the results to identify possible anomalies in the data

At the end of this tutorial, you should have a good idea of what machine learning is and will hopefully be inspired to use it to detect anomalies in your own data.

Need more context? Check out the Elasticsearch introduction to learn the lingo and understand the basics of how Elasticsearch works.


  1. Before you can play with the machine learning features, you must install Elasticsearch and Kibana. Elasticsearch stores the data and the analysis results. Kibana provides a helpful user interface for creating and viewing jobs.

    You can run Elasticsearch and Kibana on your own hardware, or use our hosted Elasticsearch Service on Elastic Cloud. The Elasticsearch Service is available on both AWS and GCP. Try out the Elasticsearch Service for free.

  2. Verify that your environment is set up properly to use the machine learning features. If the Elasticsearch security features are enabled, to complete this tutorial you need a user that has authority to manage anomaly detection jobs. See Setup.
  3. Add the sample data sets that ship with Kibana.

    1. Click the Kibana logo in the upper left hand corner of your browser to navigate to the Kibana home page.
    2. Click Load a data set and a Kibana dashboard.
    3. Pick a data set. In this tutorial, you’ll use the Sample web logs. While you’re here, feel free to click Add on all of the available sample data sets.

These data sets are now ready be analyzed in machine learning jobs in Kibana.