Azure Machine Learning: The Future of Machine Learning in the Cloud

Microsoft Azure is a Platform as a Service (PaaS) and Infrastructure as a Service (IaaS) solution created by Microsoft. Well, these days almost every next person is talking about machine learning especially deep learning, tons of books, articles, online courses, and tutorial code are also available to help whoever wants to learn about it. But if one tries to implement those algorithms and codes, it often becomes impossible if you have a low-end computing device. A decent laptop or desktop with a Nvidia GPU card costs around $2,500. Which often unaffordable to beginners, who just started to explore Deep learning.  To end this, tech giant company like Google (Google cloud), Amazon (Amazon Web Services, Inc.), Microsoft ( Microsoft Azure) have started their cloud services. Which makes computing pretty affordable if somebody lacks appropriate computing devices, all you need is efficient network access and a browser.


Microsoft Azure is a cloud computing service which is provided by the Microsoft. It provides services regarding data mining, data science, and machine learning.  The unique feature of Microsoft Azure is that there is no coding involved if you categorize your self as not an advanced user. A “Drag and Drop” approach to machine learning using a virtual graphical interface. And if you are an advanced user, you can use your scripts as well. Initially, Python was not there in this platform, but in the recent announcement, it has been notified the now Azure also supports python. Finally, you can deploy your model and use to other devices using appropriate API (C#, R or Python).


This Cloud computing service can be classified into main categories, Machine Learning service, and Machine Learning Studio.

Machine Learning service (Advance user)

  • Automated machine learning and hyper-parameter tuning
  • Choose any framework or algorithm
  • Support for popular IDEs
  • Version control and reproducibility
  • Model management
  • Hybrid deployment
  • Distributed deep learning
  • Train and deploy with ease

Machine Learning Studio

  • Serverless, drag-and-drop development
  • Code-free intuitive experimentation
  • Deploy web services in minutes

Price wise it is also quite affordable, for example, for 1 GB storage you have to pay $0.002 and $.2 per million of execution (as of today rates). One can access free training resources using this link.

Note: This is a guest post, and opinion in this article is of the guest writer. If you have any issues with any of the articles posted at please contact at


I am Nilesh Kumar, a graduate student at the Department of Biology, UAB under the mentorship of Dr. Shahid Mukhtar. I joined UAB in Spring 2018 and working on Network Biology. My research interests are Network modeling, Mathematical modeling, Game theory, Artificial Intelligence and their application in Systems Biology.

I graduated with master’s degree “Master of Technology, Information Technology (Specialization in Bioinformatics)” in 2015 from Indian Institute of Information Technology Allahabad, India with GATE scholarship. My Master’s thesis was entitled “Mirtron Prediction through machine learning approach”. I worked as a research fellow at The International Centre for Genetic Engineering and Biotechnology, New Delhi for two years.

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