Virtualization


Machine Learning Essentials - Level 2

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Shivam Sharma works as a Subject Matter Expert at CloudThat Technologies and has been involved in various large and complex projects with global clients. He has experience in Machine Learning and Microsoft Infrastructure technology stack including Azure Stack, Office 365, EMS, Lync, Exchange, System Center, Windows Servers, designing Active Directory and managing various domain services, including Hyper-V virtualization. Having core training and consulting experience, he is passionate about technology and is involved in delivering training to corporate and individuals on cutting edge technologies. Arzan has 7 years of experience in Microsoft Infrastructure technology stack including setting up Windows servers, designing Active Directory and managing various domain services, including Hyper-V virtualization. As a Cloud Solutions Architect at CloudThat, he is responsible for deploying, supporting and managing client infrastructures on Azure.


Windows Data Science Virtual Machine

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The Microsoft Data Science Virtual machine (VM) is a custom Azure VM based on Windows Server 2012 with several popular tools for data science modeling/development like: * SQL Server 2016 Developer Edition * Microsoft R server Developer Edition * Anaconda Python with Juypter notebooks * Visual Studio 2015 Community edition with language and Azure tools and * ML and Deep Learning tools like xgboost, CNTK, mxnet More information on how to use the VM can be found on the [documentation page](http://aka.ms/dsvmdoc). If are wondering about things you can do with the DSVM read this [How-To Guide to the Data Science Virtual Machine](http://aka.ms/dsvmtenthings). Here is a list of key software on the Data Science Virtual Machine and comparison between the Windows and Linux editions of the product.