In Elastic's ML, the job is the unit of work, similar to what a watch is for Elastic's alerting. As we will see in more depth later, the main configuration elements of a job are as follows:

• Job name/ID
• Analysis bucketization window (the Bucket span)
• The definition and settings for the query to obtain the raw data to be analyzed (the datafeed)
• The anomaly detection configuration recipe (the Detector)

ML jobs are independent and autonomous. Multiples can be running at once, doing independent things and analyzing data from different indices. Jobs can analyze historical data, real-time data, or a mixture of the two. Jobs can be created using the Machine Learning UI in Kibana, or programmatically via the API. They also require ML-enabled nodes.