Managing Elastic ML via the API

As with just about everything in the Elastic Stack, ML can also be completely automated via API calls—including job configuration, execution, and result gathering. Actually, all interactions you have in the Kibana UI leverage the ML API behind the scenes. You could, for example, completely write your own UI if there were specific workflows or visualizations that you wanted.

Note

For more in-depth information about the anomaly detection APIs, please refer to elastic.co/guide/en/machine-learning/current/ml-api-quickref.html. The data frame analytics part of Elastic ML has a completely separate API, which will be discussed in Chapters 9 to 13.

We won't go into each API call, but we would like to highlight some parts that are worth a detour.

The obvious first API to mention is the job creation API, which allows the creation of the ML job configuration. For example, if you wanted to recreate the population analysis job shown in Figure...