fixes

Author: lgayhardt

articles/machine-learning/how-to-create-component-pipelines-ui.md

@@ -89,7 +89,7 @@ The example below uses UI to register components, and the [component source file
     > [!Important]
     > Attached compute is not supported, use [compute instances or clusters](concept-compute-target.md#azure-machine-learning-compute-managed) instead.
 
-1. In asset library, you can see **Data**,**Model** and **Components** tabs. Switch to **Components** tab, you can see the components registered from previous section.
+1. In asset library, you can see **Data**, **Model** and **Components** tabs. Switch to **Components** tab, you can see the components registered from previous section.
 
     :::image type="content" source="./media/how-to-create-component-pipelines-ui/asset-library.png" alt-text="Screenshot showing registered component in asset library." lightbox ="./media/how-to-create-component-pipelines-ui/asset-library.png":::
 

articles/machine-learning/how-to-debug-pipeline-failure.md

@@ -51,7 +51,7 @@ If your pipeline fails or gets stuck on a node, first view the logs.
 
     The **system_logs folder** contains logs generated by Azure Machine Learning. Learn more about [View and download diagnostic logs](how-to-log-view-metrics.md#view-and-download-diagnostic-logs).
 
-    ![How to check node logs](media/how-to-debug-pipeline-failure/node-logs.gif)
+    ![Screenshot of how to check node logs.](media/how-to-debug-pipeline-failure/node-logs.gif)
 
     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.
 
@@ -150,5 +150,5 @@ To share your comparison results select **Share** and copying the link. For exam
 In this article, you learned how to debug pipeline failures. To learn more about how you can use the pipeline, see the following articles:
 
 - [How to build pipeline using python sdk v2](./how-to-create-component-pipeline-python.md)
-+ [How to build pipeline using python CLI v2](./how-to-create-component-pipelines-cli.md)
-+ [What is machine learning component](./concept-component.md)
+- [How to build pipeline using python CLI v2](./how-to-create-component-pipelines-cli.md)
+- [What is machine learning component](./concept-component.md)

articles/machine-learning/how-to-debug-pipeline-performance.md

@@ -27,7 +27,7 @@ To enable this feature:
 2. Select **Manage preview features** (megaphone icon) among the icons on the top right side of the screen.
 3. In **Managed preview feature** panel, toggle on **View profiling to debug pipeline performance issues** feature.
 
-### How to find the node that runs totally the longest
+## How to find the node that runs totally the longest
 
 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.
@@ -50,11 +50,11 @@ To enable this feature:
 
     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.
 
-### How to find the node that runs the longest in each status
+## How to find the node that runs the longest in each status
 
 Besides the total duration, you can also sort by durations for each status. For example, you can sort by *Preparing* duration to see which step spends the most time on image building. Then you can open the detail page to find that image building fails because of timeout issue.
 
-#### What do I do if a duration issue identified
+### What do I do if a duration issue identified
 
 Status and definitions:
 
@@ -66,7 +66,7 @@ Status and definitions:
 |Running | Job is executing on remote compute. Time spent in this stage is mainly in two parts: <br> Runtime preparation: image pulling, docker starting and data preparation (mount or download). <br> User script execution. | This status is expected to be most time consuming one.	| 1. Go to the source code check if there's any user error. <br>  2. View the monitoring tab of compute metrics (CPU, memory, networking etc.) to identify the bottleneck. <br> 3. Try online debug with [interactive endpoints](how-to-interactive-jobs.md) if the job is running or locally debug of your code. |
 | Finalizing | Job is in post processing after execution complete. Time spent in this stage is mainly for some post processes like: output uploading, metric/logs uploading and resources clean up.| It will be short for command job. However, might be very long for PRS/MPI job because for a distributed job, the finalizing status is from the first node starting finalizing to the last node done finalizing. | Change your step job output mode from upload to mount if you find unexpected long finalizing time, or open support case via Azure portal. |
 
-### Different view of Gantt chart
+## Different view of Gantt chart
 
 - Critical path
   - You'll see only the step jobs in the pipeline's critical path (jobs that have a dependency).
@@ -79,7 +79,7 @@ Status and definitions:
 - Hierarchical view.
   - You'll see all jobs including pipeline component jobs and step jobs.
 
-### Download the duration table
+## Download the duration table
 
 To export the table, select **Export CSV**.
 

articles/machine-learning/how-to-deploy-pipeline-component-as-batch-endpoint.md

@@ -19,6 +19,8 @@ After building your machine learning pipeline, you can deploy your pipeline as a
 - You want to run your machine learning pipeline from other platforms out of Azure Machine Learning (for example: custom Java code, Azure DevOps, GitHub Actions, Azure Data Factory). Batch endpoint lets you do this easily because it's a REST endpoint and doesn't depend on the language/platform.
 - You want to change the logic of your machine learning pipeline without affecting the downstream consumers who use a fixed URI interface.
 
+## Pipeline component deployment as batch endpoint
+
 Pipeline component deployment as batch endpoint is the feature allows you to achieve above goals. This is the equivalent feature with published pipeline/pipeline endpoint in SDK v1.
 
 To deploy your pipeline as batch endpoint, we recommend first convert your pipeline into a [pipeline component](./how-to-use-pipeline-component.md), and then deploy the pipeline component as a batch endpoint. Check below article to learn more.
@@ -30,6 +32,7 @@ It's also possible to deploy your pipeline job as batch endpoint. In this case,
 - [Deploy existing pipeline jobs to batch endpoints (preview)](how-to-use-batch-pipeline-from-job.md)
 
 ## Next steps
+
 - [How to deploy a training pipeline with batch endpoints (preview)](how-to-use-batch-training-pipeline.md)
 - [How to deploy a pipeline to perform batch scoring with preprocessing (preview)](how-to-use-batch-scoring-pipeline.md)
 - [Access data from batch endpoints jobs](how-to-access-data-batch-endpoints-jobs.md)

articles/machine-learning/toc.yml

@@ -1201,7 +1201,7 @@
             - name: How to use debug pipeline performance 
               href: how-to-debug-pipeline-performance.md
             - name: Deploy pipeline component as batch 
-              herf: how-to-deploy-pipeline-component-as-batch-endpoint.md
+              href: how-to-deploy-pipeline-component-as-batch-endpoint.md
             # v1
             - name: Create ML pipelines (Python)
               href: ./v1/how-to-create-machine-learning-pipelines.md

articles/machine-learning/v1/algorithm-cheat-sheet.md

@@ -17,11 +17,11 @@ adobe-target: true
 The **Azure Machine Learning Algorithm Cheat Sheet** helps you choose the right algorithm from the designer for a predictive analytics model.
 
 >[!Note]
-> Designer supports two types of components, classic prebuilt components（v1） and custom components(v2). These two types of components are NOT compatible. 
+> Designer supports two types of components, classic prebuilt components (v1) and custom components (v2). These two types of components are NOT compatible.
 >
 >Classic prebuilt components provide prebuilt components majorly for data processing and traditional machine learning tasks like regression and classification. This type of component continues to be supported but will not have any new components added. 
 >
->Custom components allow you to wrap your own code as a component. It supports sharing components across workspaces and seamless authoring across Studio, CLI v2, and SDK v2 interfaces. 
+>Custom components allow you to wrap your own code as a component. It supports sharing components across workspaces and seamless authoring across Studio, CLI v2, and SDK v2 interfaces.
 >
 >For new projects, we highly suggest you use custom component, which is compatible with AzureML V2 and will keep receiving new updates. 
 >

articles/machine-learning/v1/how-to-select-algorithms.md

@@ -22,7 +22,7 @@ A common question is “Which machine learning algorithm should I use?” The al
  ![Considerations for choosing algorithms: What do you want to know? What are the scenario requirements?](./media/how-to-select-algorithms/how-to-select-algorithms.png)
 
 >[!Note]
-> Designer supports two types of components, classic prebuilt components（v1） and custom components(v2). These two types of components are NOT compatible. 
+> Designer supports two types of components, classic prebuilt components (v1) and custom components (v2). These two types of components are NOT compatible. 
 >
 >Classic prebuilt components provide prebuilt components majorly for data processing and traditional machine learning tasks like regression and classification. This type of component continues to be supported but will not have any new components added. 
 >

articles/machine-learning/v1/tutorial-designer-automobile-price-deploy.md

@@ -18,7 +18,7 @@ ms.custom: designer, FY21Q4-aml-seo-hack, contperf-fy21q4, event-tier1-build-202
 Use the designer to deploy a machine learning model to predict the price of cars. This tutorial is part two of a two-part series.
 
 >[!Note]
-> Designer supports two types of components, classic prebuilt components（v1） and custom components(v2). These two types of components are NOT compatible. 
+> Designer supports two types of components, classic prebuilt components (v1) and custom components (v2). These two types of components are NOT compatible. 
 >
 >Classic prebuilt components provide prebuilt components majorly for data processing and traditional machine learning tasks like regression and classification. This type of component continues to be supported but will not have any new components added. 
 >

articles/machine-learning/v1/tutorial-designer-automobile-price-train-score.md

@@ -20,7 +20,7 @@ Train a linear regression model that predicts car prices using the Azure Machine
 This tutorial uses the Azure Machine Learning designer, for more information, see [What is Azure Machine Learning designer?](concept-designer.md)
 
 >[!Note]
-> Designer supports two types of components, classic prebuilt components（v1） and custom components(v2). These two types of components are NOT compatible. 
+> Designer supports two types of components, classic prebuilt components (v1) and custom components (v2). These two types of components are NOT compatible. 
 >
 >Classic prebuilt components provide prebuilt components majorly for data processing and traditional machine learning tasks like regression and classification. This type of component continues to be supported but will not have any new components added. 
 >