Upload Saved ML Model in R (local) to Azure Machine Learning Studio

Question

I am trying to reduce my development headaches for creating a ML Webservice on Azure ML Studio. One of the things that stuck me was can we just upload .rda files in the workbench and load it via an RScript (like in the figure below).

But can't connect directly to the R Script block. There's another way to do it (works to upload packages that aren't available in Azure's R directories) -- using zip. But there isn't really any resource out there that I found to access the .rda file in .zip.

I have 2 options here, make the .zip work or any other work around where I can directly use my .rda model. If someone could guide me about how to go forward it would appreciate it.

Note: Currently, I'm creating models via the "Create RModel" block, training them and saving it, so that I can use it to make a predictive web service. But for models like Random Forest, not sure how the randomness might create models (local versions and Azure versions are different, the setting of seed also isn't very helpful). A bit tight on schedule, Azure ML seems boxed for creating iterations and automating the ML workflow (or maybe I'm doing it wrong).

Answer 1

Here is an example of uploading a .rda file for scoring: https://gallery.cortanaintelligence.com/Experiment/Womens-Health-Risk-Assessment-using-the-XGBoost-classification-algorithm-1