What is the difference between deploying a model and selecting deploy and persist?

Answer: Deploying a machine learning run will load the results of that run into your data model, feeding any metrics, dimensions, and columns created for reporting on this data. Storyboards created from machine learning data are generally configured to display the most recent deployed run and should automatically refresh after your site has been processed.  Deploy and persist is different in that everything described above happens but the predictive model is also “frozen”.  After persisting a model, a new model will not be trained on subsequent runs.  Instead, the existing model (algorithm, settings, features, etc.) will be used.  The only thing that may change is the data you’re making predictions on.  Models that have been persisted can be unpersisted if you decide you want to train a new model by selecting the “Unpersist Current Model” button.

 

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