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Welcome to the bpmn.ai wiki!
Here you can find different information on the usage of bpmn.ai.
01_Introduction gives an overview on bpmn.ai and the Spark applications it contains.
02_How to run shows the different ways on how the Spark applications can be run (via Docker, via spark-submit, via IntelliJ/Eclipse).
03_Application parameters gives an overview on the application properties available.
04_bpmn.ai Processing Pipeline steps gives a detailed explanation on the different steps performed in bpmn.ai.
Additioanlly the are different tutorials on how to run and extend the applications as well as how to run it on AWS.
bpmn.ai is built to harvest low hanging fruits with ML. Starting is easy. Take a look at the tutorials in the wiki, to get your Camunda event history into a ML table.