Merge pull request #88272 from dagiro/cats69

cats69

Author: ktoliver

articles/hdinsight/hdinsight-log-management.md

@@ -10,6 +10,7 @@ ms.topic: conceptual
 ms.date: 03/19/2019
 ms.author: hrasheed
 ---
+
 # Manage logs for an HDInsight cluster
 
 An HDInsight cluster produces a variety of log files. For example, Apache Hadoop and related services, such as Apache Spark, produce detailed job execution logs. Log file management is part of maintaining a healthy HDInsight cluster. There can also be regulatory requirements for log archiving.  Due to the number and size of log files, optimizing log storage and archiving helps with service cost management.
@@ -74,11 +75,11 @@ Apache Ambari simplifies the management, configuration, and monitoring of a HDIn
 
 To open a list of service views, select the **Ambari Views** pane on the Azure portal page for HDInsight.  This list varies, depending on which libraries you've installed.  For example, you may see YARN Queue Manager, Hive View, and Tez View.  Select any service link to see configuration and service information.  The Ambari UI **Stack and Version** page provides information about the cluster services' configuration and service version history. To navigate to this section of the Ambari UI, select the **Admin** menu and then **Stacks and Versions**.  Select the **Versions** tab to see service version information.
 
-![Stack and Versions](./media/hdinsight-log-management/stack-versions.png)
+![Stack and Versions](./media/hdinsight-log-management/ambari-stack-versions.png)
 
-Using the Ambari UI, you can download the configuration for any (or all) services running on a particular host (or node) in the cluster.  Select the **Hosts** menu, then the link for the host of interest. On that host's page, select the **Host Actions** button and then **Download Client Configs**. 
+Using the Ambari UI, you can download the configuration for any (or all) services running on a particular host (or node) in the cluster.  Select the **Hosts** menu, then the link for the host of interest. On that host's page, select the **Host Actions** button and then **Download Client Configs**.
 
-![Host client configs](./media/hdinsight-log-management/client-configs.png)
+![Host client configs](./media/hdinsight-log-management/download-client-configs.png)
 
 ### View the script action logs
 

articles/hdinsight/hdinsight-machine-learning-overview.md

@@ -24,7 +24,7 @@ The Microsoft Machine Learning library for Apache Spark is [MMLSpark](https://gi
 
 [R](https://www.r-project.org/) is currently the most popular statistical programming language in the world. It is an open source data visualization tool with a community of over 2.5 million users and growing. With its thriving user base, and over 8,000 contributed packages, R is a likely choice for many companies who need machine learning. You can create an HDInsight cluster with ML Services ready to be used with massive datasets and models. This capability provides data scientists and statisticians with a familiar R interface that can scale on-demand through HDInsight, without the overhead of cluster setup and maintenance.
 
-![Training for prediction with R server](./media/hdinsight-machine-learning-overview/r-training.png)
+![Training for prediction with R server](./media/hdinsight-machine-learning-overview/training-for-prediction.png)
 
 The edge node of a cluster provides a convenient place to connect to the cluster and to run your R scripts.  You also have the option to run R scripts across the nodes of the cluster by using ScaleR’s Hadoop Map Reduce or Spark compute contexts.
 
@@ -34,7 +34,7 @@ With ML Services on HDInsight with Spark, you can parallelize training across th
 
 Azure Machine Learning provides tools to model predictive analytics, as well as a fully managed service you can use to deploy your predictive models as ready-to-consume web services. Azure Machine Learning is a  complete predictive analytics solution in the cloud that you can use to create, test, operationalize, and manage predictive models. Select from a large algorithm library, use a web-based studio for building models, and easily deploy your model as a web service.
 
-![Making advanced analytics accessible to Hadoop with Microsoft Azure Machine Learning](./media/hdinsight-machine-learning-overview/hadoop-azure-ml.png)
+![Making advanced analytics accessible to Hadoop with Microsoft Azure Machine Learning](./media/hdinsight-machine-learning-overview/azure-machine-learning.png)
 
 Create features for data in an HDInsight Hadoop cluster using [Hive queries](../machine-learning/team-data-science-process/create-features-hive.md). *Feature engineering* attempts to increase the predictive power of learning algorithms by creating features from raw data that facilitate the learning process. You can run HiveQL queries from Azure Machine Learning studio, and access data processed in Hive and stored in blob storage, by using the [Import Data module](../machine-learning/studio/import-data.md).