Merge pull request #106602 from Blackmist/freshness

Freshness

Author: PRMerger10

articles/machine-learning/concept-train-machine-learning-model.md

@@ -8,7 +8,7 @@ author: Blackmist
 ms.author: larryfr
 ms.subservice: core
 ms.topic: conceptual
-ms.date: 09/18/2019
+ms.date: 03/05/2020
 ---
 
 # Train models with Azure Machine Learning
@@ -24,6 +24,8 @@ Azure Machine Learning provides several ways to train your models, from code fir
     | [Estimators](#estimators) | Estimator classes **make it easy to train models based on popular machine learning frameworks**. There are estimator classes for **Scikit-learn**, **PyTorch**, **TensorFlow**, and **Chainer**. There is also a generic estimator that can be used with frameworks that do not already have a dedicated estimator class. You don't have to worry about defining a run configuration when using estimators. |
     | [Machine learning pipeline](#machine-learning-pipeline) | Pipelines are not a different training method, but a **way of defining a workflow using modular, reusable steps**, that can include training as part of the workflow. Machine learning pipelines support using automated machine learning, estimators, and run configuration to train models. Since pipelines are not focused specifically on training, the reasons for using a pipeline are more varied than the other training methods. Generally, you might use a pipeline when:<br>* You want to **schedule unattended processes** such as long running training jobs or data preparation.<br>* Use **multiple steps** that are coordinated across heterogeneous compute resources and storage locations.<br>* Use the pipeline as a **reusable template** for specific scenarios, such as retraining or batch scoring.<br>* **Track and version data sources, inputs, and outputs** for your workflow.<br>* Your workflow is **implemented by different teams that work on specific steps independently**. Steps can then be joined together in a pipeline to implement the workflow. |
 
++ [Azure Machine Learning SDK for Python](#r-sdk): The SDK uses the reticulate package to bind to Azure Machine Learning's Python SDK. This allows you access to core objects and methods implemented in the Python SDK from any R environment.
+
 + **Designer**: Azure Machine Learning designer (preview) provides an easy entry-point into machine learning for building proof of concepts, or for users with little coding experience. It allows you to train models using a drag and drop web-based UI. You can use Python code as part of the design, or train models without writing any code.
 
 + **CLI**: The machine learning CLI provides commands for common tasks with Azure Machine Learning, and is often used for **scripting and automating tasks**. For example, once you've created a training script or pipeline, you might use the CLI to start a training run on a schedule or when the data files used for training are updated. For training models, it provides commands that submit training jobs. It can submit jobs using run configurations or pipelines.
@@ -84,6 +86,15 @@ Machine learning pipelines can use the previously mentioned training methods (ru
 * [Examples: Pipeline with automated machine learning](https://aka.ms/pl-automl)
 * [Examples: Pipeline with estimators](https://aka.ms/pl-estimator)
 
+## R SDK
+
+The R SDK enables you to use the R language with Azure Machine Learning. The SDK uses the reticulate package to bind to Azure Machine Learning's Python SDK. This allows you access to core objects and methods implemented in the Python SDK from any R environment.
+
+For more information, see the following articles:
+
+* [Tutorial: Create a logistic regression model](tutorial-1st-r-experiment.md)
+* [Azure Machine Learning SDK for R reference](https://azure.github.io/azureml-sdk-for-r/index.html)
+
 ## Azure Machine Learning designer
 
 The designer lets you to train models using a drag and drop interface in your web browser.

articles/machine-learning/how-to-deploy-custom-docker-image.md

@@ -9,7 +9,7 @@ ms.topic: conceptual
 ms.author: jordane
 author: jpe316
 ms.reviewer: larryfr
-ms.date: 08/22/2019
+ms.date: 03/05/2020
 ---
 
 # Deploy a model using a custom Docker base image

articles/machine-learning/how-to-enable-logging.md

@@ -8,7 +8,7 @@ ms.service: machine-learning
 ms.subservice: core
 ms.topic: conceptual
 ms.reviewer: trbye
-ms.date: 07/12/2019
+ms.date: 03/05/2020
 ---
 
 # Enable logging in Azure Machine Learning
@@ -96,7 +96,7 @@ You can also log custom stack traces for your web service by enabling Applicatio
 service.update(enable_app_insights=True)
 ```
 
-See the [how-to](how-to-enable-app-insights.md) for more information on how to work with Application Insights in Azure Machine Learning studio.
+For more information, see [Monitor and collect data from ML web service endpoints](how-to-enable-app-insights.md).
 
 ## Python native logging settings
 
@@ -106,3 +106,7 @@ Certain logs in the SDK may contain an error that instructs you to set the loggi
 import logging
 logging.basicConfig(level=logging.DEBUG)
 ```
+
+## Next steps
+
+* [Monitor and collect data from ML web service endpoints](how-to-enable-app-insights.md)

articles/machine-learning/how-to-secure-web-service.md

@@ -1,7 +1,7 @@
 ---
-title: Secure web services using SSL
+title: Secure web services using TLS
 titleSuffix: Azure Machine Learning
-description: Learn how to enable HTTPS in order to secure a web service that's deployed through Azure Machine Learning. 
+description: Learn how to enable HTTPS in order to secure a web service that's deployed through Azure Machine Learning. Azure Machine Learning uses TLS version 1.2 to secure models deployed as web services.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -10,11 +10,11 @@ ms.topic: conceptual
 ms.reviewer: jmartens
 ms.author: aashishb
 author: aashishb
-ms.date: 08/12/2019
+ms.date: 03/05/2020
 ms.custom: seodec18
 ---
 
-# Use SSL to secure a web service through Azure Machine Learning
+# Use TLS to secure a web service through Azure Machine Learning
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
 This article shows you how to secure a web service that's deployed through Azure Machine Learning.

articles/machine-learning/tutorial-train-deploy-model-cli.md

@@ -32,7 +32,7 @@ Learn how to take the following actions:
 
 ## Prerequisites
 
-* An Azure subscription. If you don’t have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
+* An Azure subscription. If you don't have an Azure subscription, create a free account before you begin. Try the [free or paid version of Azure Machine Learning](https://aka.ms/AMLFree) today.
 
 * To use the CLI commands in this document from your **local environment**, you need the [Azure CLI](https://docs.microsoft.com/cli/azure/install-azure-cli?view=azure-cli-latest).
 
@@ -370,7 +370,7 @@ az ml model deploy -n myservice -m "mymodel:1" --ic inferenceConfig.yml --dc aci
 ```
 
 > [!NOTE]
-> You may receive a warning about "Failed to check LocalWebservice existence". You can safely ignore this, as you are not deploying a local web service.
+> You may receive a warning about "Failed to check LocalWebservice existence" or "Failed to create Docker client". You can safely ignore this, as you are not deploying a local web service.
 
 This command deploys a new service named `myservice`, using version 1 of the model that you registered previously.