Azure ML Package is another Microsoft related package that allows you to upload and download datasets to and from AzureML, to interrogate experiments, to publish R functions as AzureML web services, and to run R data through existing web services and retrieve the output.
Azure ML in R studio
The first step- Installation
First, you need to install.package(“AzureML”) command to install this package into your R version.
using below command:
Second Step- get the ID and Authentication from Azure ML
the second step is to get some of the identification from the Azure ML studio setting. you need to navigate to “studio.azureml.net”
in your Azure ML environment click on the setting, there is a “workspaceid” copy the number.
in R studio assign the number to a variable as below
id<- "your workspace ID"
then navigate to another tab in setting menu and choose the “Authentication tokens”, copy the primary authentication token and allocate it to a variable in R studio.
auth<-"your authorization token"
There is a function in AzureML package name “workspace” that creates a reference to an AzureML Studio workspace by getting the authentication token and workspace id as below:
ws <- workspace( id , auth )
to work with other AzureML packages you need to pass this object to them.
for instance for exploring the all experiments in Azure ML there is a function name “experiments” that gets the “ws” object as input to connect the desire azure ml environment and also a filter.
Step Three- Browse the Experiments
experiments(ws, filter = "all")
as you know in Azure ML studio we have 2 experiment type ” samples” and “my experiment” .
in experiments function you able to mention which experiment you want to see the details, so the filter values will be “all”, “samples” and “my datasets”.
I set it to “all”. by applying the “names” function, you able to see the columns: the experimentation is, name, versions, owner, and so forth.
Step Four- Upload a dataset and Browse Datasets
there is a possibility to upload a dataset from R studio into Azure ML by command upload.dataset
there is a free dataset in “ggplot2” package, that I am going to upload it to the Azure Ml studio.
you need to provides the dataset, the workstation objects (ws), and a name that you want to save the dataset in Azure ML environment.
Now if you explore the datasets in Azure ML, you able to see the new uploaded dataset there.
dataAzureML<-datasets(ws,filter = "my datasets")
some of the information such as name, size and so forth will be shown.
IN the next posts, I will show how to upload a function as web service into Azure ML environment.