Merge pull request #110041 from lobrien/1694164-DSVM-OS-Nudge

Edit to overview description; grammar and style

Author: PRMerger9

articles/machine-learning/data-science-virtual-machine/overview.md

@@ -1,7 +1,7 @@
 ---
 title: What is the Azure Data Science Virtual Machine
 titleSuffix: Azure Data Science Virtual Machine 
-description: Key analytics scenarios and components for Windows and Linux Data Science Virtual Machines.
+description: Overview of Azure Data Science Virtual Machine - An easy to create and use virtual machine on the Azure cloud platform with preinstalled and configured tools and libraries for doing data science and developing intelligent applications.
 keywords: data science tools, data science virtual machine, tools for data science, linux data science
 services: machine-learning
 ms.service: machine-learning
@@ -10,7 +10,7 @@ ms.subservice: data-science-vm
 author: vijetajo
 ms.author: vijetaj
 ms.topic: overview
-ms.date: 12/31/2019
+ms.date: 04/02/2020
 
 ---
 
@@ -19,17 +19,17 @@ ms.date: 12/31/2019
 The Data Science Virtual Machine (DSVM) is a customized VM image on the Azure cloud platform built specifically for doing data science. It has many popular data science tools preinstalled and preconfigured to jumpstart building intelligent applications for advanced analytics. 
 
 The DSVM is available on:
+
 + **Windows Server 2019**
 + **Ubuntu 18.04 LTS**
 + Windows Server 2016
 + Ubuntu 16.04 LTS and CentOS 7.4
 
-
 > [!NOTE]
 > All VM tools for deep learning have been folded into the Data Science Virtual Machine. 
 
-
 ## Why choose the DSVM?
+
 The goal of the Data Science Virtual Machine is to provide data professionals of all skill levels and across industries with a friction-free, preconfigured data science environment. Instead of rolling out a comparable workspace on your own, you can provision a DSVM. That choice can save you days or even _weeks_ on the installation, configuration, and package management processes. After your DSVM has been allocated, you can immediately begin working on your data science project.
 
 ## Sample Use Cases
@@ -41,33 +41,37 @@ Below, we illustrate some common use cases for DSVM customers.
 The DSVM provides a baseline configuration for data science teams that want replace their local desktops with a managed cloud desktop, ensuring that all the data scientists on a team have a consistent setup with which to verify experiments and promote collaboration. It also lowers costs by reducing the sysadmin burden. This burden reduction saves on the time needed to evaluate, install, and maintain software packages for advanced analytics.
 
 ### Data science training and education
+
 Enterprise trainers and educators who teach data science classes usually provide a virtual machine image. The image ensures that students have a consistent setup and that the samples work predictably. 
 
 The DSVM creates an on-demand environment with a consistent setup that eases the support and incompatibility challenges. Cases where these environments need to be built frequently, especially for shorter training classes, benefit substantially.
 
 ### On-demand elastic capacity for large-scale projects
+
 Data science hackathons/competitions or large-scale data modeling and exploration require scaled-out hardware capacity, typically for short duration. The DSVM can help replicate the data science environment quickly on demand, on scaled-out servers that allow experiments that  high-powered computing resources can run.
 
 ### Custom compute power for Azure Notebooks
+
 [Azure Notebooks](../../notebooks/azure-notebooks-overview.md) is a free hosted service to develop, run, and share Jupyter notebooks in the cloud with no installation. The free service  tier is limited to 4 GB of memory and 1 GB of data. 
 
 To release all limits, you can attach a Notebooks project to a DSVM or any other VM running on a Jupyter server. If you sign in to Azure Notebooks with an account by using Azure Active Directory (such as a corporate account), Notebooks automatically shows DSVMs in any subscriptions associated with that account. You can [attach a DSVM to Azure Notebooks](../../notebooks/configure-manage-azure-notebooks-projects.md#compute-tier) to expand the available compute power.
 
 ### Short-term experimentation and evaluation
+
 You can use the DSVM to evaluate or learn new data science [tools](./tools-included.md), especially by going through some of our published [samples and walkthroughs](./dsvm-samples-and-walkthroughs.md).
 
 
 ### Deep learning with GPUs
+
 In the DSVM, your training models can use deep learning algorithms on hardware that's based on graphics processing units (GPUs). By taking advantage of the VM scaling capabilities of the Azure platform, the DSVM helps you use GPU-based hardware in the cloud according to your needs. You can switch to a GPU-based VM when you're training large models, or when you need high-speed computations while keeping the same OS disk. You can choose any of the N series GPU enabled virtual machine SKUs with DSVM. Please note Azure free accounts do not support GPU enabled virtual machine SKUs.
 
 The Windows Server 2016 edition of the DSVM comes pre-installed with GPU drivers, frameworks, and GPU versions of deep learning frameworks. On the Linux edition, deep learning on GPUs is enabled on both the CentOS and Ubuntu DSVMs. 
 
 You can also deploy the Ubuntu, CentOS, or Windows 2016 edition of the DSVM to an Azure virtual machine that isn't based on GPUs. In this case, all the deep learning frameworks will fall back to the CPU mode.
- 
+
 [Learn more about available deep learning and AI frameworks](dsvm-tools-deep-learning-frameworks.md).
 
 <a name="included"></a>
-
 ## What's included on the DSVM?
 
 See a full list of tools on both the Windows and Linux DSVM's [here](tools-included.md).

articles/machine-learning/data-science-virtual-machine/tools-included.md

@@ -92,7 +92,7 @@ The Data Science Virtual Machine comes with the most useful data-science tools p
 | &nbsp;&nbsp;&nbsp;&nbsp; [Sparkmagic](https://github.com/jupyter-incubator/sparkmagic)                                                                                |                                                                                  <span class='red-x'>&#10060;</span>                                                                                 |                                                    <span class='green-check'>&#9989;</span></br> (Ubuntu only)                                                   |                                                                                                                                                                  |                                                                                                                                |                                                                                                                             |
 | &nbsp;&nbsp;&nbsp;&nbsp; SparkR                                                                                                                                       |                                                                                  <span class='red-x'>&#10060;</span>                                                                                 |                                                             <span class='green-check'>&#9989;</span>                                                             |                                                                                                                                                                   |                                                                                                                          |                                                                                                                             |
 
-**Ubuntu 18.04 DSVM and Windows Server 2019 DSVM** has following Jupyter Kernels:-</br> 
+**Ubuntu 18.04 DSVM and Windows Server 2019 DSVM** has the following Jupyter Kernels:-</br> 
 * Python 3.7 - default</br>  
 * Python 3.7 - PyTorch</br>  
 * Python 3.7 - TensorFlow</br>  
@@ -106,20 +106,20 @@ The Data Science Virtual Machine comes with the most useful data-science tools p
 * Scala Spark – HDInsight</br>  
 * Python 3 Spark – HDInsight</br>  
 
-**Ubuntu 18.04 DSVM and Windows Server 2019 DSVM** has following conda environments:-</br> 
+**Ubuntu 18.04 DSVM and Windows Server 2019 DSVM** has the following conda environments:-</br> 
 * py37_default  </br>
 * py37_tensorflow </br> 
 * py37_pytorch  </br>
 * azureml_py36_tensorflow  </br>
 * azureml_py36_pytorch  </br>
 * azureml_py36_automl  </br> 
 
-**Ubuntu 16.04 DSVM** has following conda environments:-</br>   
+**Ubuntu 16.04 DSVM** has the following conda environments:-</br>   
 * base  </br>
 * py37 </br>
 * azureml_py36 </br>
 
-**Windows Server 2016** has following conda environments:-</br>  
+**Windows Server 2016** has the following conda environments:-</br>  
 * base   </br>
 * AzureML  </br>
 * python2  </br>