Merge pull request #418 from MicrosoftDocs/master

8/5/2019 AM Publish

docs/data-guide/technology-choices/data-science-and-machine-learning.md

@@ -56,7 +56,7 @@ The following development platforms and tools are available for machine learning
 
 [Azure Machine Learning service](/azure/machine-learning/service/overview-what-is-azure-ml) is a fully managed cloud service used to train, deploy, and manage machine learning models at scale. It fully supports open-source technologies, so you can use tens of thousands of open-source Python packages such as TensorFlow, PyTorch, and scikit-learn. Rich tools are also available, such as [Azure notebooks](https://notebooks.azure.com/), [Jupyter notebooks](http://jupyter.org), or the [Azure Machine Learning for Visual Studio Code](https://aka.ms/vscodetoolsforai) extension to make it easy to explore and transform data, and then train and deploy models. Azure Machine Learning service includes features that automate model generation and tuning with ease, efficiency, and accuracy.
 
-Use Azure Machine Learning service to train, deploy, and manage machine learning models using Python and CLI at cloud scale. For a low-code or no-code option, use the interactive, [visual interface](/azure/machine-learning/service/ui-quickstart-run-experiment) (preview) to easily and quickly build, test, and deploy models using pre-built machine learning algorithms.
+Use Azure Machine Learning service to train, deploy, and manage machine learning models using Python and CLI at cloud scale. For a low-code or no-code option, use the interactive, [visual interface](/azure/machine-learning/service/ui-tutorial-automobile-price-train-score) (preview) to easily and quickly build, test, and deploy models using pre-built machine learning algorithms.
 
 Try the [free or paid version of Azure Machine Learning service](https://aka.ms/AMLFree).
 
@@ -119,7 +119,7 @@ Use SQL Server Machine Learning Services when you need built-in AI and predictiv
 
 ## Microsoft Machine Learning Server
 
-[Microsoft Machine Learning Server](https://docs.microsoft.com/machine-learning-server/what-is-machine-learning-server) is an enterprise server for hosting and managing parallel and distributed workloads of R and Python processes. Microsoft Machine Learning Server runs on Linux, Windows, Hadoop, and Apache Spark, and it is also available on [HDInsight](https://azure.microsoft.com/services/hdinsight/r-server/) as [Microsoft Machine Learning Server (ML Server)](https://docs.microsoft.com/en-us/azure/hdinsight/r-server/r-server-overview). It provides an execution engine for solutions built using [RevoScaleR](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/revoscaler), [revoscalepy](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/revoscalepy-package), and  [MicrosoftML packages](https://docs.microsoft.com/r-server/r/concept-what-is-the-microsoftml-package), and extends open-source R and Python with support for high-performance analytics, statistical analysis, machine learning, and massively large datasets. This functionality is provided through proprietary packages that install with the server. For development, you can use IDEs such as [R Tools for Visual Studio](https://www.visualstudio.com/vs/rtvs/) and [Python Tools for Visual Studio](https://www.visualstudio.com/vs/python/).
+[Microsoft Machine Learning Server](https://docs.microsoft.com/machine-learning-server/what-is-machine-learning-server) is an enterprise server for hosting and managing parallel and distributed workloads of R and Python processes. Microsoft Machine Learning Server runs on Linux, Windows, Hadoop, and Apache Spark, and it is also available on [HDInsight](https://azure.microsoft.com/services/hdinsight/r-server/) as [Microsoft Machine Learning Server (ML Server)](https://docs.microsoft.com/azure/hdinsight/r-server/r-server-overview). It provides an execution engine for solutions built using [RevoScaleR](https://docs.microsoft.com/machine-learning-server/r-reference/revoscaler/revoscaler), [revoscalepy](https://docs.microsoft.com/machine-learning-server/python-reference/revoscalepy/revoscalepy-package), and  [MicrosoftML packages](https://docs.microsoft.com/r-server/r/concept-what-is-the-microsoftml-package), and extends open-source R and Python with support for high-performance analytics, statistical analysis, machine learning, and massively large datasets. This functionality is provided through proprietary packages that install with the server. For development, you can use IDEs such as [R Tools for Visual Studio](https://www.visualstudio.com/vs/rtvs/) and [Python Tools for Visual Studio](https://www.visualstudio.com/vs/python/).
 
 Use Microsoft Machine Learning Server when you need to build and operationalize models built with R and Python on a server, or distribute R and Python training at scale on a Hadoop or Spark cluster.
 

docs/toc.experimental.yml

@@ -1357,14 +1357,14 @@ items:
         items:
         - name: Introduction
           href: cloud-adoption/operations/monitor/index.md
-        - name: Platform overview
-          href: cloud-adoption/operations/monitor/platform-overview.md
-        - name: Monitoring cloud apps
-          href: cloud-adoption/operations/monitor/cloud-app-howto.md
+        - name: Monitoring cloud models
+          href: cloud-adoption/operations/monitor/cloud-models-monitor-overview.md
         - name: Data collection
           href: cloud-adoption/operations/monitor/data-collection.md
         - name: Alerting
           href: cloud-adoption/operations/monitor/alert.md
+        - name: Monitoring platforms overview
+          href: cloud-adoption/operations/monitor/platform-overview.md
       - name: Establish an operational fitness review
         href: cloud-adoption/operations/operational-fitness-review.md
   - name: References