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What is Best Practice for streamed apply of MLTK models after removal of streaming_apply?


Hello Splunk Community!

In MLTK version 5.3.1, the streaming_apply feature was removed due to bundle replication performance issues. However, I am currently facing a problem where executing a continuously updated model in a distributed fashion across all available search peers in our Splunk Enterprise setup would be highly beneficial.

As information on this former functionality appears sparse, I wanted to inquire regarding best way to handle automatically replicating the trained model to the search peers and executing it there, if it is at all still possible.

A previous question asked here (How to export/import/share ML models between Splunk instances and external system? ) hinted at manually copying the model files into the target lookup folder as an alternative to using streaming_apply. With daily updates to the model, this is sadly not an option in our deployment.

Thanks for your help!

Best regards


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