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There doesn't seem to be too many options for deploying predictive models in production which is surprising given the explosion in Big Data.
I understand that the open-source PMML can be used to export models as an XML specification. This can then be used for in-database scoring/prediction. However it seems that to make this work you need to use the PMML plugin by Zementis which means the solution is not truly open source. Is there an easier open way to map PMML to SQL for scoring?
Another option would be to use JSON instead of XML to output model predictions. But in this case, where would the R model sit? I'm assuming it would always need to be mapped to SQL...unless the R model could sit on the same server as the data and then run against that incoming data using an R script?
Any other options out there?
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Don't get me wrong, I also agree that R can be definitely used in production. Indeed, I plan to use R in production for data products myself where I work, since the development stack is C# and not python anyway, and we're expanding our R skills by hiring more people.
Model deployment is the process of putting machine learning models into production. This makes the model's predictions available to users, developers or systems, so they can make business decisions based on data, interact with their application (like recognize a face in an image) and so on.
The following is a list of the alternatives that I have found so far to deploy an R model in production. Please note that the workflow to use these products varies significantly between each other, but they are all somehow oriented to facilitate the process of exposing a trained R model as a service:
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