Once again, Microsoft is ringing its cloud computing bell as the company attempts to pivot and transition towards a cloud services enterprise.
A long-standing tent pole of Microsoft's push into cloud computing involves the use of its services in the research arena and the company is now offering a new tutorial to researchers looking to leverage the cloud.
From Azure to web apps to Linux Virtual Machines, Microsoft's Azure 4 Researcher contributing author Dr. Kenji Takeda walk researchers through the process of using Azure.
- Watch this short video about the Azure portal, which is the easiest user interface. There is also a command-line interface for Linux, Mac and Windows, as well as language APIs for Python, Java, and other languages.
- Follow this Azure storage hands-on tutorial that will take around 30 mins. This is important so you understand how to easily work with files and storage, which is one of the most useful features of the cloud.
- Launch your first Azure virtual machine (VM) – a remote workstation in the cloud. There are a number of pre-built images (VMs with software pre-installed). A good one to start with is the Linux Data Science VM also the Windows version here. You can create a basic VM (e.g. Ubuntu Linux) and install whatever software you like by following these instructions. We suggest that you start with a small VM (A1) to get started while you are getting to grips with the basics. You can easily rebuild/reboot a bigger VM (many cores and large memory) once you are familiar with the environment.
- Try the Jupyter Notebooks-as-a-Service on Azure. They are free, executable and shareable over the web. Organize your notebooks and datasets in one centralized location. Libraries are saved automatically and can be viewed from any device, anywhere. These are a great way to do research in a reproducible way. You can start from scratch or upload your existing notebooks to Azure at https://notebooks.azure.com .
- Explore Azure Machine Learning that is a complete end-to-end, easy to use, web-based system to experiment with your own machine learning algorithms. It makes it easy to test and deploy machine learning models, including with your own Python and R code using standard libraries like Sci-Kit Learn. There are many walkthroughs and tutorials to get you started. See https://studio.azureml.net/
Takeda continues on encouraging those interested in more Azure information to check out a few more Azure for Research video walkthroughs.
The Azure for Researcher blog promises more videos and tutorials to come that focus on helping those transitioning to the use of cloud services in the near future.