I went through the manuals available on the IBM website. However, there is no explanation on how to annotate a model for R API users. Does anyone have a hint?
Related
tl;dr I want to deploy "live" model results in Python and R, and while Salesforce Einstein advertises this functionality for R and Python, I have only found support for Python. Shiny is too expensive to justify for our limited R-language requirements. Does Einstein R support actually exist?
UPDATE: Tableau has a separate solution from Einstein Analytics that hosts both R and Python - see answer below. Not a feature-rich direct competitor to Shiny, but that's not our use-case.
According to the documentation for Salesforce Einstein Analytics Plus (aka Tableau CRM AI Analytics), data scientists can upload (operationalize) their Python, R, and Matlab code, as described here:
https://www.tableau.com/solutions/ai-analytics (see the section on "Data Science" at the bottom of the page).
I signed up for a trial of Einstein Analytics Plus, and found a link to the "Model Manager." Using Model Manager to deploy Python-language models is well-documented here:
https://help.salesforce.com/s/articleView?id=sf.bi_edd_model_upload_prepare.htm&type=5
For Python, this seems to match the advertised functionality. But there is no indication of how to deploy R language models, which may be part of my team's use case.
I would like to find the equivalent method for deploying an R-language model in Einstein. Particularly, is there some other Salesforce / Tableau product I should try, or is this a feature that is simply not available in the trial version. Unlike Python deployment, searching the documentation has not yielded answers.
Alternatively, we're only interested in Einstein R support is because it appears to be about 1/10 the cost of Shiny, which is hideously expensive. So any recommendations regarding lightweight alternatives to Shiny would also be helpful.
TIA for anyone who can shine a light on this problem.
ANSWER: There is actually a separate feature in Tableau that is different from Einstein Analytics which supports both R and Python, documentation here:
https://help.tableau.com/current/prep/en-us/prep_scripts.htm
I am working with a tool written in Microsoft Access and my job is to replace some of the graphs/charts in the tool with graphs created in ggplot2 in R.
I can create the graphs in R and copy paste it to the tool but the problem is the tool is used by people who are not familiar with R language so I need to make an automation connecting R and Microsoft Access. Has anyone did this before? Thanks for your help.
Breeze
So, I'm working on a big data project which requires importing data into Visual Studio and using Microsoft's R packages, in particular, the RevoScaleR packages. So, this is all well and good. I've written my scripts. What I would like to do is set it up so an end-user (who doesn't know anything about programming or R) can enter a set of parameters/values into my predict() function and see the output on his/her screen through a web interface or GUI/similar.
Any suggestions?
Thanks.
You can achieve this by leveraging Microsoft R Server's Operationalization Features, as this is a general question, please see the guides here:
https://msdn.microsoft.com/en-us/microsoft-r/operationalize/quickstart-publish-web-service
Is there a pmml conversion for the bnlearn package in r, so that bayesian networks can be accessed as an API over the web?
I have seen pmml conversion for some models like decision trees and linear regression, see here, but I haven't seen any for bnlearn.
Also, is there any other service which can be used as an API for bayesian network models built in R?
I haven't tried this personally but the package r2pmml from GitHub (it is not available from CRAN) may be an option for this. You would then you "Augustus" in Python.
I do a lot of computational intelligence research. I have used Matlab almost exclusively as my programming medium for a decade or so. I am now trying to move to OSS. I have settled on R as my new environment.
After a long search for neural net software, the only Matlab-comparable OSS packages are Stuttgart NN and FANN (this can be debated another time =). The former doesn't appear to be maintained so I'd like to go with the latter. So my question is:
Does anyone have experience using R and FANN?
FANN has C++ bindings and R seems to have a couple of packages for a C++ interface, but since I'm a R newbie I need an idea of where exactly to start. Any guidance or recommendations would be appreciated.
Cheers.
I do not know anything abuot FANN but I can assure you that R has an actively maintained interface to the Stuttgart Neural Net Simulator (SNNS) library via the
RSNNS package --- as RSNNS happens to employ the
Rcpp package for interfacing R and C++ which I am involved in.