At the R/Finance conference last month, I demonstrated how to operationalize models developed in Microsoft R Server as web services using the mrsdeploy package. Then, I used that deployed model to generate predictions for loan delinquency, using a Python script as the client. (You can see slides here, and a video of the presentation below.)
- Flexible Operationalization: Deploy any R script or function.
- Real-Time Operationalization: Deploy model objects generated by specific functions in Microsoft R, but generates predictions much more quickly by bypassing the R interpreter.
In the demo, which begins at the 10:00 mark in the video below, you can see a comparison of using the two types of deployment. Ultimately, I was able to generate predictions from a random forest at a rate of 1M predictions per second, with three Python clients simultaneously drawing responses from the server (an Azure GS5 instance running the Windows Data Science VM).
If you'd like to try out this capability yourself, you can find the R and Python scripts used in the demo at this Github repository. The lending club data is available here, and the script used to featurize the data is here.