Merge remote-tracking branch 'refs/remotes/MicrosoftDocs/master'

Author: anzaman

articles/machine-learning/service/concept-automated-ml.md

@@ -33,10 +33,11 @@ Data scientists, analysts and developers across industries can use automated ML
 
 The following table lists common automated ML use cases. 
 
-Classification| Regression | Time series forecasting
+Classification| Time series forecasting | Regression
 ---|---|---
-[Fraud Detection](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)|[CPU Performance Prediction](https://github.com/Azure/MachineLearningNotebooks/blob/37541b10714f9337dbbae721bea494272dc7d151/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb) |[Demand Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)|
-[Marketing Prediction](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)|[Material Durability Prediction](https://github.com/Azure/MachineLearningNotebooks/blob/37541b10714f9337dbbae721bea494272dc7d151/how-to-use-azureml/automated-machine-learning/regression-concrete-strength/auto-ml-regression-concrete-strength.ipynb)|[Sales Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)
+[Fraud Detection](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-credit-card-fraud/auto-ml-classification-credit-card-fraud.ipynb)|[Sales Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-orange-juice-sales/auto-ml-forecasting-orange-juice-sales.ipynb)|[CPU Performance Prediction](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/regression-hardware-performance-explanation-and-featurization/auto-ml-regression-hardware-performance-explanation-and-featurization.ipynb)
+|[Marketing Prediction](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-bank-marketing-all-features/auto-ml-classification-bank-marketing-all-features.ipynb)|[Demand Forecasting](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-energy-demand/auto-ml-forecasting-energy-demand.ipynb)|
+|[Newsgroup Data Classification](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/classification-text-dnn/auto-ml-classification-text-dnn.ipynb)|[Beverage Production Forecast](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/automated-machine-learning/forecasting-beer-remote/auto-ml-forecasting-beer-remote.ipynb)|
 
 ## How automated ML works
 

articles/machine-learning/service/concept-compute-instance.md

@@ -8,33 +8,34 @@ ms.subservice: core
 ms.topic: conceptual
 ms.author: sgilley
 author: sdgilley
-ms.date: 11/04/2019
+ms.date: 12/13/2019
 # As a data scientist, I want to know what a compute instance is and how to use it for Azure Machine Learning.
 ---
 
 # What is an Azure Machine Learning compute instance?
 
-An Azure Machine Learning compute instance (preview) is a fully managed cloud-based workstation for data scientists. Compute instance makes it  easy to get started with Azure Machine Learning development. Compute instance provides management and enterprise readiness capabilities for IT administrators.  Use a compute instance as your fully configured and managed development environment in the cloud.
+An Azure Machine Learning compute instance (preview) is a fully-managed cloud-based workstation for data scientists. 
+
+Compute instances make it easy to get started with Azure Machine Learning development as well as provide management and enterprise readiness capabilities for IT administrators.  
+
+Use a compute instance as your fully configured and managed development environment in the cloud.
+
+Compute instances are typically used as development environments.  They can also be used as a compute target for training and inferencing for development and testing.  For large tasks, an [Azure Machine Learning compute cluster](how-to-set-up-training-targets.md#amlcompute) with multi-node scaling capabilities is a better compute target choice.
 
 > [!NOTE]
 > Compute instances are currently available only for workspaces with a region of **North Central US** or **UK South**, with support for other regions coming soon.
 >If your workspace is in any other region, you can continue to create and use a [Notebook VM](concept-compute-instance.md#notebookvm) instead. 
 
 ## Why use a compute instance?
 
-A compute instance is a managed virtual machine (VM), optimized to be your machine learning development environment in the cloud. It provides the following benefits:
+A compute instance is a fully-managed cloud-based workstation optimized for your machine learning development environment. It provides the following benefits:
 
-* **Productive**: Data scientists can build and deploy models using integrated notebooks and the following tools in their web browser:
-    * Jupyter
-    * JupyterLab
-    * RStudio
-* **Managed and secure**:  Reduce your security footprint and add compliance with enterprise security requirements. Compute instances  provide robust management policies and secure networking configurations such as:
-    * Automated provisioning through Resource Manager templates or Azure Machine Learning SDK
-    * [Role-based access control (RBAC)](/azure/role-based-access-control/overview).
-    * Virtual network support. You can [create a compute instance in a virtual network](how-to-enable-virtual-network.md#compute-instance).
-    * SSH policy to enable/disable SSH access
-* **Preconfigured for machine learning**: Save time on setup tasks with pre-configured and up-to-date ML packages, deep learning frameworks, GPU drivers.
-* **Fully customizable**: Broad support for Azure VM types including GPUs and persisted low-level customization such as installing packages and drivers makes advanced scenarios a breeze. 
+|Key benefits||
+|----|----|
+|Productivity|Data scientists can build and deploy models using integrated notebooks and the following tools in their web browser:<br/>-  Jupyter<br/>-  JupyterLab<br/>-  RStudio|
+|Managed & secure|Reduce your security footprint and add compliance with enterprise security requirements. Compute instances  provide robust management policies and secure networking configurations such as:<br/><br/>- Auto-provisioning from Resource Manager templates or Azure Machine Learning SDK<br/>- [Role-based access control (RBAC)](/azure/role-based-access-control/overview)<br/>- [Virtual network support](how-to-enable-virtual-network.md#compute-instance)<br/>- SSH policy to enable/disable SSH access|
+|Preconfigured&nbsp;or&nbsp;ML|Save time on setup tasks with pre-configured and up-to-date ML packages, deep learning frameworks, GPU drivers.|
+|Fully customizable|Broad support for Azure VM types including GPUs and persisted low-level customization such as installing packages and drivers makes advanced scenarios a breeze. |
 
 ## <a name="contents"></a>Tools and environments
 
@@ -43,9 +44,7 @@ Azure Machine Learning compute instance enables you to author, train, and deploy
 
 These tools and environments are installed on the compute instance: 
 
-
-
-|General tools & environments|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Details&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|
+|General tools & environments|Details|
 |----|:----:|
 |Drivers|`CUDA`</br>`cuDNN`</br>`NVIDIA`</br>`Blob FUSE` |
 |Intel MPI library||
@@ -57,7 +56,7 @@ These tools and environments are installed on the compute instance:
 |NCCL 2.0 ||
 |Protobuf|| 
 
-|**R** tools & environments|&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;Details&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;|
+|**R** tools & environments|Details|
 |----|:----:|
 |RStudio Server Open Source Edition||
 |R kernel||
@@ -76,7 +75,6 @@ These tools and environments are installed on the compute instance:
 |ONNX packages|`keras2onnx`</br>`onnx`</br>`onnxconverter-common`</br>`skl2onnx`</br>`onnxmltools`|                           
 |Azure Machine Learning Python & R SDK samples||
 
-Compute instances are typically used as development environments.  They can also be used as a compute target for training and inferencing for development and testing.  For large tasks, an [Azure Machine Learning compute cluster](how-to-set-up-training-targets.md#amlcompute) with multi-node scaling capabilities is a better compute target choice.
 
 
 ## Accessing files
@@ -110,7 +108,7 @@ For each compute instance in your workspace you can:
 * SSH into compute instance. SSH access is disabled by default but can be enabled at compute instance creation time. SSH access is through public/private key mechanism. The tab will give you details for SSH connection such as IP address, username, and port number.
 * Get details about a specific compute instance such as IP address, and region.
 
-[RBAC](/azure/role-based-access-control/overview) allows you to control which users in the workspace can create, delete, start, stop, restart a compute instance. All users in the workspace contributor and owner role can create, delete, start, stop, and restart compute instances across the workspace. However, only the creator of a specific compute instance is allowed to access Jupyter, JupyterLab, and RStudio on that compute instance. The creator of the compute instance has the compute instance dedicated to them, have root access, and can terminal in through Jupyter. Compute instance will have single-user login of creator user and all actions will use that user’s identity for RBAC and attribution of experiment runs. SSH acess is controlled through public/private key mechanism.
+[RBAC](/azure/role-based-access-control/overview) allows you to control which users in the workspace can create, delete, start, stop, restart a compute instance. All users in the workspace contributor and owner role can create, delete, start, stop, and restart compute instances across the workspace. However, only the creator of a specific compute instance is allowed to access Jupyter, JupyterLab, and RStudio on that compute instance. The creator of the compute instance has the compute instance dedicated to them, have root access, and can terminal in through Jupyter. Compute instance will have single-user login of creator user and all actions will use that user’s identity for RBAC and attribution of experiment runs. SSH access is controlled through public/private key mechanism.
 
 You can also create an instance
 * Directly from the integrated notebooks experience