Data Simulator for Machine Learning

Virtually any data science experiment that uses a new machine learning algorithm requires testing across different scenarios. Simulated data allows one to do this in a controlled and systematic way that is usually not possible with real data.

A convenient way to implement and re-use data simulation in Azure Machine Learning (AML) Studio is through a custom R module. Custom R modules combine the convenience of having an R script packaged inside a drag and drop module, with the flexibility of custom code where the user has the freedom of adding and removing functionality parameters, seen as module inputs in the AML Studio GUI, as needed. A custom R module has identical behavior to native AML Studio modules. Its input and output can be connected to other modules or be set manually, and they can process data of arbitrary schema, if the underlying R code allows it, inside AML experiments. An added benefit is that they provide a convenient way of deploying code without revealing the source, which may be convenient for IP sensitive scenarios. By publishing it in Cortana Intelligence Gallery one can easily expose to the world any algorithm functionality without worrying about classical software deployment process.

 

Read more on the https://azure.microsoft.com/en-us/blog/data-simulator-for-machine-learning-custom-r-module-cortana-intelligence-gallery/