updates

Author: v-albemi

articles/machine-learning/tutorial-cloud-workstation.md

@@ -36,7 +36,7 @@ You can create compute resources in the **Compute** section in your workspace. A
 1. Sign in to [Azure Machine Learning studio](https://ml.azure.com).
 1. Select your workspace, if it isn't already open.
 1. In the left pane, select **Compute**.
-1. If you don't have a compute instance, you see **New** in the middle of the screen. Select **New** and fill out the form. You can use all the defaults.
+1. If you don't have a compute instance, you see **New** in the middle of the page. Select **New** and fill out the form. You can use all the defaults.
 1. If you have a compute instance, select it from the list. If it's stopped, select **Start**.
 
 ## Open Visual Studio Code (VS Code)
@@ -58,7 +58,7 @@ In order for your script to run, you need to be working in an environment that's
     Files that you upload are stored in an Azure file share, and these files are mounted to each compute instance and shared within the workspace.
 
     1. Go to [azureml-examples/tutorials/get-started-notebooks/workstation_env.yml](https://github.com/Azure/azureml-examples/blob/main/tutorials/get-started-notebooks/workstation_env.yml).
-    1. Download the Conda environment file [*workstation_env.yml*](https://github.com/Azure/azureml-examples/blob/main/tutorials/get-started-notebooks/workstation_env.yml) to your computer selecting the ellipsis button (**...**) in the top-right corner of the page and then selecting **Download**.
+    1. Download the Conda environment file *workstation_env.yml* to your computer by selecting the ellipsis button (**...**) in the top-right corner of the page and then selecting **Download**.
     1. Drag the file from your computer to the Visual Studio Code window. The file is uploaded to your workspace.
     1. Move the file into your username folder.
 
@@ -88,7 +88,7 @@ In order for your script to run, you need to be working in an environment that's
         cd Users/myusername
         ```
 
-    1. Make sure the workstation_env.yml is in the folder.
+    1. Make sure workstation_env.yml is in the folder.
 
         ```bash
         ls
@@ -132,7 +132,7 @@ You now have a new kernel. Next, you'll open a notebook and use this kernel.
 
 ## Set the kernel
 
-1. On the top-right corner of the new file, select **Select Kernel**.
+1. In the top-right corner of the new file, select **Select Kernel**.
 1. Select **Azure ML compute instance (computeinstance-name)**.
 1. Select the kernel you created: **Tutorial Workstation Env**. If you don't see the kernel, select the refresh button above the list.
 
@@ -154,7 +154,7 @@ This code uses `sklearn` for training and MLflow for logging the metrics.
 
     [!notebook-python[] (~/azureml-examples-main/tutorials/get-started-notebooks/cloud-workstation.ipynb?name=extract)]
 
-1. Add code to start autologging with MLflow so that you can track the metrics and results. With the iterative nature of model development, MLflow helps you log model parameters and results. Refer to the runs to compare and understand how your model performs. The logs also provide context for when you're ready to move from the development phase to the training phase of your workflows within Azure Machine Learning.
+1. Add code to start autologging with MLflow so that you can track the metrics and results. With the iterative nature of model development, MLflow helps you log model parameters and results. Refer to different runs to compare and understand how your model performs. The logs also provide context for when you're ready to move from the development phase to the training phase of your workflows within Azure Machine Learning.
 
     [!notebook-python[] (~/azureml-examples-main/tutorials/get-started-notebooks/cloud-workstation.ipynb?name=mlflow)]
 
@@ -187,7 +187,7 @@ Now that you've tried two different models, use the results tracked by MLFfow to
     :::image type="content" source="media/tutorial-cloud-workstation/jobs.png" alt-text="Screenshot that shows the Jobs item in the left pane.":::
 
 1. Select **Develop on cloud tutorial**.
-1. There are two different jobs shown, one for each of the models you tried. The names are autogenerated. If you want to rename the job, hover over the name and select the pencil button next to it. 
+1. There are two jobs shown, one for each of the models you tried. The names are autogenerated. If you want to rename the job, hover over the name and select the pencil button next to it. 
 1. Select the link for the first job. The name appears at the top of the page. You can also rename it here by using the pencil button.
 1. The page shows job details, like properties, outputs, tags, and parameters. Under **Tags**, you see the **estimator_name**, which describes the type of model.
 1. Select the **Metrics** tab to view the metrics that were logged by MLflow. (Your results will be different because you have a different training set.)
@@ -238,7 +238,7 @@ Go back to **Jobs** in your workspace in Azure Machine Learning studio to see th
 
 If you plan to continue on to other tutorials, skip to [Next steps](#next-steps).
 
-### Stop compute instance
+### Stop the compute instance
 
 If you're not going to use it now, stop the compute instance: