It would be exceptionally complicated to attempt to recreate an exact datarobot model within SAS.
However - when you deploy a model in DataRobot it gives you an API end point which you can use to make predictions based on new data.
I suggest that you call this API from SAS to apply the predictions to your data as a data connection. In datarobot, if you navigate to the "integrations" tab within your deployment, it will show you the Python Code needed to connect to the API. From this you can get the information you need (key, endpoint, formats etc) that you can use to plug into your SAS ETL process.
Scoring the model inside DataRobot
If SAS is part of your pre/post-processing, you can still leverage model deployment and scoring on DataRobot; the process would be as follows:
This requires two tasks;
The above is highly recommended as the solution for scoring data through a SAS pipeline. It takes advantage of deploying a model for usage with just a few clicks in the GUI, and that model is monitored over time for data drift and target drift, to let you know when your model may be getting stale and in need of retraining and replacement.
Scoring the model inside SAS
Premium versions of DataRobot include exportable versions of code, including Java binaries as well as a python rules based approximation the latter called DataRobot Prime. It is not the exact model, but an approximated one to a model you base it on from the leaderboard. It will receive its own leaderboard entry. It would be possible to create a Prime model, export the code, at which point you could then port it over to SAS code. This is a labor intensive process with possible introduction for error unless done programmatically however, and would come at the loss of the simple integrated drift tracking enjoyed by the DataRobot hosted deployment. Some features are also not available; for example, the Prediction Explanations for a scored record are only available via the Prediction API and DataRobot deployment, but not from Prime.
Note that both scenarios could be run daily with new data.
I am a new user of DR.
The reason for transfer would be two: 1) company requirement; 2) need to run the model every day with new data.
My need is to get the SAS data on a server, run the model and then return the data to a table on the SAS server. This being done every day.
Hi Carlos - is there a reason you are looking to transfer the model into SAS, rather use the model in DataRobot? Note that you can prepare data in SAS, send it to DataRobot for scoring, and retrieved scored results in SAS for further use. This would allow for you to easily take advantage of a developed and deployed model within DataRobot, as well as enjoy the benefits of that deployment being tracked for data drift of the features input to the model.