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Yes code in both. Depends on who I am working with really, prefer R but python much easier to integrate in a production environment.

I don’t have a need personally but some companies that are doing industrial scale modeling (on the order of building and maintaining thousands of models) do use a build server to Basically check that code is formatted properly and can have a model run in a somewhat automated fashion.

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What makes productionizing R difficult?

I'm curious about whether the rise of Python in data science is really just because of the lack of flexibility to integrate with other systems. I've read that R seems to do better in data science/analytics work but when it comes to integration, it's more challenging.

And any insights on which companies are already doing industrial scale modeling?

Do you use Anaconda's R. If so I'd like to know of any rough edges (package count being a known one we're looking to address).

Thanks.

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