Options for deploying R models in production

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:

• openCPU
• AzureML
• DeployR
• yhat (already mentioned by @Ramnath)
• Domino
• Sense.io

A common practice is scoring a new/updated dataset in R and moving only the results (IDs, scores, probabilities, other necessary fields) into the production environment/data warehouse.

I know this has its limitations (infrequent refreshes, reliance upon IT, data set size/computing power restrictions) and may not be the cutting edge answer many (of your bosses) are looking for; but for many use-cases this works well (and is cost friendly!).

You can create RESTful APIs for your R scripts using plumber (https://github.com/trestletech/plumber).

I wrote a blog post about it (http://www.knowru.com/blog/how-create-restful-api-for-machine-learning-credit-model-in-r/) using deploying credit models as an example.

In general, I do not recommend PMML because the packages you used might not support translation to PMML.

The answer really depends on what your production environment is.

If your "big data" are on Hadoop, you can try this relatively new open source PMML "scoring engine" called Pattern.

Otherwise you have no choice (short of writing custom model-specific code) but to run R on your server. You would use save to save your fitted models in .RData files and then load and run corresponding predict on the server. (That is bound to be slow but you can always try and throw more hardware at it.)

How you do that really depends on your platform. Usually there is a way to add "custom" functions written in R. The term is UDF (user-defined function). In Hadoop you can add such functions to Pig (e.g. https://github.com/cd-wood/pigaddons) or you can use RHadoop to write simple map-reduce code that would load the model and call predict in R. If your data are in Hive, you can use Hive TRANSFORM to call external R script.

There are also vendor-specific ways to add functions written in R to various SQL databases. Again look for UDF in the documentation. For instance, PostgreSQL has PL/R.