fixing

lgayhardt · lgayhardt · commit 9f54cd791a0f · 2023-05-27T17:05:26.000-07:00
diff --git a/articles/machine-learning/concept-designer.md b/articles/machine-learning/concept-designer.md
@@ -19,8 +19,6 @@ Azure Machine Learning designer is a drag-and-drop UI interface to build pipelin
 
 As shown in below GIF, you can build a pipeline visually by drag and drop your building blocks and connect them.
 
-![Azure Machine Learning designer example](./media/concept-designer/designer-drag-and-drop.gif)
-
 :::image type="content" source="./media/concept-designer/designer-drag-and-drop.gif" alt-text="GIF of a building a pipeline in the designer." lightbox= "./media/concept-designer/designer-drag-and-drop.gif":::
 
 
diff --git a/articles/machine-learning/how-to-debug-pipeline-failure.md b/articles/machine-learning/how-to-debug-pipeline-failure.md
@@ -9,7 +9,7 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: how-to
-ms.date: 12/22/2022
+ms.date: 05/27/2023
 ms.custom: designer, event-tier1-build-2022
 ---
 
@@ -25,9 +25,6 @@ This article introduces how to use the pipeline job page to debug machine learni
 > For more information, see [Supplemental Terms of Use for Microsoft Azure Previews](https://azure.microsoft.com/support/legal/preview-supplemental-terms/).
 
 
-
-
-
 ## Using outline to quickly find a node
 
 In pipeline job detail page, there's an outline left to the canvas, which shows the overall structure of your pipeline job. Hovering on any row, you can select the "Locate" button to locate that node in the canvas.
@@ -58,8 +55,6 @@ If your pipeline fails or gets stuck on a node, first view the logs.
 
     If you don't see those folders, this is due to the compute run time update isn't released to the compute cluster yet, and you can look at **70_driver_log.txt** under **azureml-logs** folder first.
 
-
-
 ## Compare different pipelines to debug failure or other unexpected issues (preview)
 
 Pipeline comparison identifies the differences (including topology, component properties, and job properties) between multiple jobs. For example you can compare a successful pipeline and a failed pipeline, which helps you find what modifications make your pipeline fail.
@@ -150,8 +145,6 @@ To share your comparison results select **Share** and copying the link. For exam
 
 :::image type="content" source="./media/how-to-debug-pipeline-failure/share.png" alt-text="Screenshot showing the share button and the link you should copy." lightbox= "./media/how-to-debug-pipeline-failure/share.png":::
 
-
-
 ## Next steps
 
 In this article, you learned how to debug pipeline failures. To learn more about how you can use the pipeline, see the following articles:
diff --git a/articles/machine-learning/how-to-debug-pipeline-performance.md b/articles/machine-learning/how-to-debug-pipeline-performance.md
@@ -9,7 +9,7 @@ services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
 ms.topic: how-to
-ms.date: 04/28/2023
+ms.date: 05/27/2023
 ms.custom: designer, pipeline UI
 ---
 # View profiling to debug pipeline performance issues (preview)
@@ -32,14 +32,12 @@ To enable this feature:
 1. On the Jobs page, select the job name and enter the job detail page.
 1. In the action bar, select **View profiling**. Profiling only works for root level pipeline. It will take a few minutes to load the next page.
 
-    ![Screenshot showing the pipeline at root level with the view profiling button highlighted.](./media/how-to-debug-pipeline-performance/view-profiling.png)
-
-   
+    :::image type="content" source="./media/how-to-debug-pipeline-performance/view-profiling.png" alt-text="Screenshot showing the pipeline at root level with the view profiling button highlighted." lightbox= "./media/how-to-debug-pipeline-performance/view-profiling.png":::
 
 1. After the profiler loads, you'll see a Gantt chart. By Default the critical path of a pipeline is shown. A critical path is a subsequence of steps that determine a pipeline job's total duration.
 
-    ![Screenshot showing the Gantt chart and the critical path.](./media/how-to-debug-pipeline-performance/critical-path.png)
-  
+    :::image type="content" source="./media/how-to-debug-pipeline-performance/critical-path.png" alt-text="Screenshot showing the Gantt chart and the critical path." lightbox= "./media/how-to-debug-pipeline-performance/critical-path.png":::
+
 1. To find the step that takes the longest, you can either view the Gantt chart or the table below it.
 
     In the Gantt chart, the length of each bar shows how long the step takes, steps with a longer bar length take more time. You can also filter the table below by "total duration". When you select a row in the table, it shows you the node in the Gantt chart too. When you select a bar on the Gantt chart it will also highlight it in the table.
@@ -48,8 +46,7 @@ To enable this feature:
 
     If you select the log icon next the node name it opens the detail page, which shows parameter, code, outputs, logs etc.
 
-    ![Screenshot highlighting the log icon and showing the detail page.](./media/how-to-debug-pipeline-performance/detail-page-from-log-icon.png)
-
+    :::image type="content" source="./media/how-to-debug-pipeline-performance/detail-page-from-log-icon.png" alt-text="Screenshot highlighting the log icon and showing the detail page." lightbox= "./media/how-to-debug-pipeline-performance/detail-page-from-log-icon.png":::
 
     If you're trying to make the queue time shorter for a node, you can change the compute node number and modify job priority to get more compute resources on this one.
 
@@ -86,7 +83,7 @@ Status and definitions:
 
 To export the table, select **Export CSV**.
 
-![screenshot show export csv in profiling](./media/how-to-debug-pipeline-performance/export-csv.png)
+:::image type="content" source="./media/how-to-debug-pipeline-performance/export-csv.png" alt-text="Screenshot show export csv in profiling." lightbox= "./media/how-to-debug-pipeline-performance/export-csv.png":::
 
 ## Next steps
 
diff --git a/articles/machine-learning/v1/concept-designer.md b/articles/machine-learning/v1/concept-designer.md
@@ -31,7 +31,7 @@ Azure Machine Learning designer is a drag-and-drop interface used to train and d
  - To get started with the designer, see [Tutorial: Train a no-code regression model](tutorial-designer-automobile-price-train-score.md). 
  - To learn about the components available in the designer, see the [Algorithm and component reference](../algorithm-module-reference/module-reference.md).
 
-![Azure Machine Learning designer example](../media/concept-designer/designer-drag-and-drop.gif)
+:::image type="content" source="../media/concept-designer/designer-drag-and-drop.gif" alt-text="GIF of a building a pipeline in the designer." lightbox= "../media/concept-designer/designer-drag-and-drop.gif":::
 
 The designer uses your Azure Machine Learning [workspace](../concept-workspace.md) to organize shared resources such as:
 
@@ -55,7 +55,7 @@ Use a visual canvas to build an end-to-end machine learning workflow. Train, tes
     + Publish a **batch inference pipeline** to make predictions on new data by using a previously trained model.
 + [Deploy](#deploy) a **real-time inference pipeline** to an online endpoint to make predictions on new data in real time.
 
-![Workflow diagram for training, batch inference, and real-time inference in the designer](../media/concept-designer/designer-workflow-diagram.png)
+:::image type="content" source="../media/concept-designer/designer-workflow-diagram.png" alt-text="Workflow diagram for training, batch inference, and real-time inference in the designer.":::
 
 ## Pipeline
 
@@ -88,9 +88,9 @@ A machine learning data asset makes it easy to access and work with your data. S
 
 A component is an algorithm that you can perform on your data. The designer has several components ranging from data ingress functions to training, scoring, and validation processes.
 
-A component may have a set of parameters that you can use to configure the component's internal algorithms. When you select a component on the canvas, the component's parameters are displayed in the Properties pane to the right of the canvas. You can modify the parameters in that pane to tune your model. You can set the compute resources for individual components in the designer. 
+A component may have a set of parameters that you can use to configure the component's internal algorithms. When you select a component on the canvas, the component's parameters are displayed in the Properties pane to the right of the canvas. You can modify the parameters in that pane to tune your model. You can set the compute resources for individual components in the designer.
 
-:::image type="content" source="../media/concept-designer/properties.png" alt-text="Component properties":::
+:::image type="content" source="../media/concept-designer/properties.png" alt-text="Screenshot showing the component properties.":::
 
 
 For some help navigating through the library of machine learning algorithms available, see [Algorithm & component reference overview](../component-reference/component-reference.md). For help with choosing an algorithm, see the [Azure Machine Learning Algorithm Cheat Sheet](algorithm-cheat-sheet.md).
