The Execute R Script module in Azure Machine Learning is incredibly useful for manipulating data in ways that other modules do not cover. Its functionality can be further expanded by adding R packages that are not included in Azure ML by default. We will first show you how to get a list of packages that are already in your workspace and then how to add additional packages.
Checking Which R Packages are in Your Workspace
Create a new experiment, and place the following R code in an “Execute R Script” module:
Run the experiment. The output of the Execute R Script module will be a list of the available packages.
Adding R Packages
Before you can use the package in Azure ML, you need to set up the zip file structure in which ML expects the packages to appear. To do this, start by installing the…
While working with Azure Machine Learning, we ran into some situations that seem simple to handle in hindsight, but are rarely discussed online. Here are some simple Azure ML tips and tricks that we found useful.
1. Saving and Reusing a Trained Model
In one of our projects, we found that we needed to use the same trained model multiple times rather than just once in a predictive experiment. To do this, save a trained model by right clicking the output port of the Train Model module and selecting the Save as Trained Model option.
After giving the trained model a name and saving it, you will be able to find a module with the given name under the Trained Models sections of the module selection bar
or by typing the name into the module search bar. From here, you will be able to use…
Azure Machine Learning is part of the Cortana Intelligence Suite. It’s a cloud based collaborative drag and drop tool that can be used to build, test, and deploy predictive analytics solutions. We recently worked with Casella Waste Systems to analyze their customer and sales data using Azure ML. We found Azure ML to be a useful tool that allowed us to visualize our work and avoid coding when it wasn’t necessary. However, the process of creating successful experiments didn’t come without some speed bumps. Here are some things that we discovered along our ML journey, delivered in the spirit of a Buzzfeed article.
1. Your experiment starts out nice and simple
2. And ends up looking like this
3. You have multiple people working on an experiment
4. But you see this when everyone tries to edit the experiment at the same time