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?
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:
This post was edited by Shivakumar Kota at October 15, 2020 5:31 PM ISTThe 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 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.
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!).
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.