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What is the best upgrade path for Machine Learning Toolkit in a distributed deployment?

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Let's say you have a few search heads and ten or so indexers all running Splunk 6.2, and you want to upgrade your environment in the least intrusive way so you can use the Machine Learning Toolkit. First off, is the Machine Learning Toolkit (and underlying Python for Scientific Computing add-on) only compatible with Splunk 6.5 and up? Or can it work on 6.4 as well? And more importantly, does it come built-in with Splunk 6.5 or does it (and the scientific computing add-on) still have to be installed by hand after upgrading to 6.5? If so, is there a good way to automate that process? Overall, what is the safest and most efficient approach for upgrading this kind of environment to leverage the new functionality? What kinds of hitches would you be likely to encounter?

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