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Microsoft Azure: Remote Desktop Connection to Data Science Windows 2016 Virtual Machine

January 11, 2019 , , ,
The key advantage of setting up Microsoft Azure Data Science Windows 2016 Virtual Machine instead of a regular virtual machine is that it comes pre-installed with major data science tools which saves a lot of time for making data science machines functional to start doing the actual data science work. Microsoft Azure Data Science Windows 2016 Virtual Machine also comes pre-installed with compute GPU based on NVidia drivers as a unified support for deep learning data science problems evaluation and solution.


Here is the list of some of the data science related tools that come pre-installed with Microsoft Azure Data Science Windows 2016 Virtual Machine i.e.
  1. Microsoft R Server - Developer Edition (Scalable R).
  2. Anaconda Python.
  3. SQL Server 2017 - Developer Edition with in-database R & Python analytics.
  4. Microsoft Office 365 ProPlus - Shared Computer Activation.
  5. Julia Pro + Juno Editor.
  6. Jupyter Notebooks.
  7. Visual Studio Community Edition with Python, R & Node.js tools.
  8. Power BI Desktop.
  9. Weka.
  10. Git.
  11. R Stidio.
  12. NVidia drivers, CUDA toolkit & NVidia CUDNN library for GPU pre-configure & tested.
  13. Deep learning tools e.g. Microsoft Cognitive toolkit (CNTK 2.1), TensorFlow & Mxnet.
  14. Machine Learning algorithm libraries e.g. Xgboost, Vowpal Wabbit.
  15. Azure SDK & libraries for various cloud offerings. Integration tools are included for
    1. Azure Machine Learning.
    2. Azure Data Factory.
    3. Stream Analytics.
    4. SQL Data Warehouse.
    5. Hadoop + Apache Spark (HDInsight Cluster).
    6. Data Lake.
    7. Blob Storage.
    8. Machine Learning & Data Science Tutorials as Jupyter Notebooks.
In today's article I shall demonstrate to make remote desktop connection to Microsoft Azure data science windows 2016 virtual machine into our Microsoft Azure paid subscription account. 

Prerequisites:

Following are some prerequisites before you proceed any further in this tutorial:
  1. Microsoft Azure Account with Paid Subscription.
  2. You have already created your target Data Science Windows 2016 Virtual Machine on Azure Portal.
Let's begin the fun.

1) Create your target Azure Data Science Windows 2016 Virtual Machine on your Microsoft Azure paid subscription account.

2) Login to Microsoft azure portal and you will be able to see your dashboard.

3) Click on your "Azure Data Science Windows 2016 Virtual Machine" from your dashboard as shown below i.e.


4) Now, click "Connect" on your "Azure Data Science Windows 2016 Virtual Machine" overview page as shown below i.e.


From the above screen, you have connection information to access your data science windows 2016 virtual machine. Copy your machine public IP address.

5) Go to your machine start menu and click "Run" command or using shortcut key "Windows button + R" to make "Run" window appear as shown below i.e.


6) Now, type "mstsc" on Run command window as shown below i.e.


7) In "Remote Desktop Connection" windows type your data science virtual machine IP address and click "Connect" as shown below i.e.


8) In the next window, type your administration credentials which you have setup during the installation of your data science virtual machine and click "OK" as shown below i.e.


9) When you connection is successful, following window will appear, click "Yes" as shown below i.e.


10) Congratulation, you are connected to your "Azure Data Science Windows 2016 Virtual Machine" as shown below i.e.


11) Now, explore your data science virtual machine, check your system specification, details of your RAM and processors, your hard disk information and your pre-installed data science tools as shown below i.e.


Conclusion

In this article, you will learn to make remote desktop connection to data science windows 2016 virtual machine. You will learn to use Run command, you will learn to use remote desktop connection and you will learn to know about your virtual machine by exploring it via checking your system specification, details of your RAM and processors, your hard disk information and your pre-installed data science tools.

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