Merge branch 'getroyer/DSVMFileSystem' of https://github.com/getroyer/azure-docs-pr into getroyer/DSVMFileSystem

Author: getroyer

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

@@ -15,15 +15,18 @@ ms.workload: data-services
 ms.tgt_pltfrm: na
 ms.devlang: na
 ms.topic: conceptual
-ms.date: 03/16/2018
+ms.date: 02/22/2019
 ms.author: gokuma
 
 ---
 # Get to know your Data Science Virtual Machine
 
 The Data Science Virtual Machine (DSVM) includes an extensive list of popular data science, machine learning/AI tools, and data platforms. The tool configurations are rigorously tested by data scientists and developers at Microsoft and by the broader data science community to ensure stability and general viability.
 
-The DSVM also includes multiple code samples and walkthroughs in the form of Jupyter Notebooks that can be executed locally. There is also documentation for these samples to aid comprehension. 
+The DSVM also includes multiple code samples and walkthroughs in the form of Jupyter Notebooks that can be executed locally.There is also documentation for these samples to aid comprehension. 
+
+> [!NOTE]
+> You can use [Azure Notebooks](/azure/notebooks/azure-notebooks-overview.md) to create, run, and share Jupyter notebooks in the cloud. You can [attach a Data Science VM to Azure Notebooks](/azure/notebooks/configure-manage-azure-notebooks-projects.md#compute-tier) to expand the available compute power.
 
 The tools, platforms, utilities, and samples that are included in the Data Science VM are organized into the following sections:
 

articles/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro.md

@@ -115,6 +115,8 @@ You can access the Ubuntu DSVM using three methods:
 1. X2Go for graphical sessions
 1. JupyterHub and JupyterLab for Jupyter notebooks
 
+You can also attach a Data Science VM to Azure Notebooks to run Jupyter notebooks on the VM and bypass the limitations of the free service tier. For more information, see [Manage and configure Notebooks projects - Compute tier](/azure/notebooks/configure-manage-azure-notebooks-projects.md#compute-tier).
+
 ### SSH
 
 After the VM is created, you can sign in to it by using SSH. Use the account credentials that you created in the **Basics** section of step 3 for the text shell interface. On Windows, you can download an SSH client tool like [Putty](https://www.putty.org). If you prefer a graphical desktop (X Windows System), you can use X11 forwarding on Putty or install the X2Go client.

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

@@ -14,7 +14,7 @@ ms.workload: data-services
 ms.tgt_pltfrm: na
 ms.devlang: na
 ms.topic: overview
-ms.date: 10/27/2017
+ms.date: 02/22/2019
 ms.author: gokuma
 
 ---
@@ -43,6 +43,10 @@ Enterprise trainers and educators that teach data science classes usually provid
 ### 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 Data Science VM can help replicate the data science environment quickly on demand, on scaled out servers that allow experiments requiring high-powered computing resources to be run.
 
+### Custom compute power for Azure Notebooks
+
+[Azure Notebooks](/azure/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, however,  is limited to 4GB of memory and 1GB of data. To release all limits, you can then attach a Notebooks project to a Data Science VM or any other VM running Jupyter server. If you sign into Azure Notebooks with an account using Azure Active Directory (such as a corporate account), Notebooks automatically shows Data Science VMs in any subscriptions associated with that account. For more information, see [Manage and configure projects - Compute tier](/azure/notebooks/configure-manage-azure-notebooks-projects.md#compute-tier).
+
 ### Short-term experimentation and evaluation
 The Data Science VM can be used to evaluate or learn tools such as Microsoft ML Server, SQL Server, Visual Studio tools, Jupyter, deep learning / ML toolkits, and new tools popular in the community with minimal setup effort. Since the Data Science VM can be set up quickly, it can be applied in other short-term usage scenarios like replicating published experiments, executing demos, following walkthroughs in online sessions and conference tutorials.
 

articles/machine-learning/data-science-virtual-machine/provision-vm.md

@@ -14,7 +14,7 @@ ms.subservice: data-science-vm
 ms.workload: data-services
 ms.devlang: na
 ms.topic: article
-ms.date: 12/04/2018
+ms.date: 02/22/2019
 ms.author: gokuma
 
 ---
@@ -103,6 +103,8 @@ To create a DSVM instance:
 
 After the VM is created and provisioned, you can remote desktop into it by using the admin account credentials that you configured in the preceding **Basics** section. You're ready to start using the tools that are installed and configured on the VM. Many of the tools can be accessed through start menu tiles and desktop icons.
 
+You can also attach a Data Science VM to Azure Notebooks to run Jupyter notebooks on the VM and bypass the limitations of the free service tier. For more information, see [Manage and configure Notebooks projects - Compute tier](/azure/notebooks/configure-manage-azure-notebooks-projects.md#compute-tier).
+
 ## Tools installed on the Microsoft Data Science Virtual Machine
 
 Learn more about the tools that come installed on the DSVM: