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Our Talking About R… series continues! Today we are focusing on integrating R with Azure Machine Learning. See how you can use R programming in Azure Machine Learning for solving a variety of data modelling issues, sales analysis, text analysis, or forecasting.
Azure Machine Learning is a fully cloud-based service, used to gather the magic flowing from your data. The human eye is unable to extract non-obvious patterns from data, especially if the number of rows in the table exceeds the measurable time – welcome to the big data world where R programming is an inseparable element of data analysis.
Let’s try it – it’s free! 🙂
R in Machine Learning can be used in 3 ways:
I will explain each of these methodologies shortly. It is very important when building an analytical solution in Azure Machine Learning, because R can extend capabilities of the service. Azure Machine Learning has a collection of functionalities and a strictly limited list of models – that’s why R is needed!
When forecasting time series data or making your own recommender system, using R script is crucial for implementing an appropriate machine learning model.
Azure ML hasn’t got models for forecasting time series or building recommendations based on association rules – you have to use R script and make your own model.
The most commonly used R component in Azure Machine Learning is probably R Script.
Using R Script we are able to implement a wide range of data processing capabilities available in R. However, you should keep in mind that R Script also has a limited number of libraries available.
R Script in Azure ML is mainly used for data cleaning methods where a more complex processing logic is required. Moreover, you can generate statistics or data visualization while running an experiment, because R Script can also return a graph which we can access from the R Device.
We can therefore use it for:
It is possible to create additional models in Azure Machine Learning using R Models. It strengthens R’s position relative to Python, because there is only one Python script component in Azure Machine Learning.
The Create R Models component contains two elements that the user fills in with R code:
There are some restrictions with using R:
You can easily check the list of supported packages by calling the following command:
data.set <- data.frame(installed.packages()) maml.mapOutputPort("data.set")
These are pre-loaded packages, so referencing them only requires entering library() command in the R Script with the package name.
On the msdn website dedicated to the service, we can find the following information about the limitations of the packages:
“A number of packages are included in the Azure Machine Learning environment but cannot be called from R code because of the following issues:
The list of packages supported by Azure Machine Learning is listed on the official website under this link.
This specific package has been created especially for the operationalization of the Azure Machine Learning service. It allows you to execute a lot of useful functions, such as:
Before you start the journey with AzureML package you should be sure that your environment is ready and that you have your own account on the Azure Machine Learning Service. The second thing – it is very important to ensure that you have a zip utility on your system.
If you encounter the error Requires external zip utility. Please install zip, ensure it’s on your path and try again, do the following:
A very good description of the package with examples is found in the CRAN repository – the document is created in RMarkDown and is very user-friendly.
For more information, check out this link. If you need more explanation about AzureML functions or configurations, let me know!
As we’ve learnt from the Microsoft Ignite conference sessions, there are many improvements or add-ons that will make this tool even more useful.
“The Azure Machine Learning Experimentation service allows developers and data scientists to increase their rate of experimentation. With every project backed by a Git repository, and with a simple command line tool for managing experimentation and training runs, every execution can track the code, configuration, and data that’s used for the run. More importantly, the outputs of that experiment, from model files, log output, and key metrics are tracked, giving you a powerful repository with the history of how your model evolves over time. (…)
Model management service provides deployment, hosting, versioning, management, and monitoring for models in Azure, on-premises, and to IOT Edge devices. (…)
Azure Machine Learning Workbench with AI powered data wrangling is a client application that runs on Windows and Macs. It has an easy set-up and installation and will install a configured Python environment, complete with conda, Jupyter, and more, along with connectivity to all of the backend services in Azure.”
“Visual Studio Code Tools for AI is an extension to build, test, and deploy Deep Learning / AI solutions. It seamlessly integrates with Azure Machine Learning for robust experimentation capabilities, including but not limited to submitting data preparation and model training jobs transparently to different compute targets. Additionally, it provides support for custom metrics and run history tracking, enabling data science reproducibility and auditing. Enterprise ready collaboration, allow to securely work on project with other people.” (Source)
All of the above capabilities are detailed in this article.
The key things you need to know are:
Azure ML is a user-friendly interface for conducting Machine Learning experiments. It is an extensible environment using R scripts or Python. Do not waste your time and try it out!
I look forward to your feedback! 🙂 Get in touch now!
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