fix blocking and non-blocking issues

Author: sdgilley

articles/machine-learning/service/tutorial-1st-experiment-sdk-train.md

@@ -121,11 +121,11 @@ After the training has completed, call the `experiment` variable to fetch a link
 experiment
 ```
 
-<table style="width:100%"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>diabetes-experiment</td><td>your-workspace-name</td><td>Link to Azure Portal</td><td>Link to Documentation</td></tr></table>
+<table style="width:100%"><tr><th>Name</th><th>Workspace</th><th>Report Page</th><th>Docs Page</th></tr><tr><td>diabetes-experiment</td><td>your-workspace-name</td><td>Link to Azure portal</td><td>Link to Documentation</td></tr></table>
 
 ## View training results in portal
 
-Following the **Link to Azure Portal** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.
+Following the **Link to Azure portal** takes you to the main experiment page. Here you see all the individual runs in the experiment. Any custom-logged values (`alpha_value` and `rmse`, in this case) become fields for each run, and also become available for the charts and tiles at the top of the experiment page. To add a logged metric to a chart or tile, hover over it, click the edit button, and find your custom-logged metric.
 
 When training models at scale over hundreds and thousands of separate runs, this page makes it easy to see every model you trained, specifically how they were trained, and how your unique metrics have changed over time.
 
@@ -165,7 +165,7 @@ print("Best run_id rmse: " + str(minimum_rmse))
     Best run_id: 864f5ce7-6729-405d-b457-83250da99c80
     Best run_id rmse: 57.234760283951765
 
-Use the best run id to fetch the individual run using the `Run` constructor along with the experiment object. Then call `get_file_names()` to see all the files available for download from this run. In this case, you only uploaded one file for each run during training.
+Use the best run ID to fetch the individual run using the `Run` constructor along with the experiment object. Then call `get_file_names()` to see all the files available for download from this run. In this case, you only uploaded one file for each run during training.
 
 ```python
 from azureml.core import Run

includes/aml-create-in-portal.md

@@ -35,7 +35,7 @@ ms.date: 08/14/2019
 1. After you are finished configuring the workspace, select **Create**. 
 
    > [!Warning] 
-   > It can take a several minutes to create your workspace in the cloud.
+   > It can take several minutes to create your workspace in the cloud.
 
    When the process is finished, a deployment success message appears.