updates

Author: v-albemi

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

@@ -175,91 +175,89 @@ Now that you have model results, you might want to change something and try agai
 
 ## Examine the results
 
-Now that you've tried two different models, use the results tracked by MLFfow to decide which model is better. You can reference metrics like accuracy, or other indicators that matter the most for your scenarios. You can dive into these results in more detail by looking at the jobs created by `MLflow`.
+Now that you've tried two different models, use the results tracked by MLFfow to decide which model is better. You can reference metrics like accuracy, or other indicators that matter the most for your scenarios. You can review these results in more detail by looking at the jobs created by MLflow.
 
 1. Return to your workspace in the [Azure Machine Learning studio](https://ml.azure.com).
-1. On the left navigation, select **Jobs**.
+1. In the left pane, select **Jobs**.
 
-    :::image type="content" source="media/tutorial-cloud-workstation/jobs.png" alt-text="Screenshot shows how to select Jobs in the navigation.":::
+    :::image type="content" source="media/tutorial-cloud-workstation/jobs.png" alt-text="Screenshot that shows the Jobs item in the left pane.":::
 
-1. Select the link for **Develop on cloud tutorial**.
-1. There are two different jobs shown, one for each of the models you tried. These names are autogenerated. As you hover over a name, use the pencil tool next to the name if you want to rename it. 
-1. Select the link for the first job. The name appears at the top. You can also rename it here with the pencil tool.
-1. The page shows details of the job, such as properties, outputs, tags, and parameters. Under **Tags**, you'll see the estimator_name, which describes the type of model.
+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 tool 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 there by using the pencil tool.
+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.)
 
-1. Select the **Metrics** tab to view the metrics that were logged by `MLflow`. (Expect your results to differ, as you have a different training set.)
+    :::image type="content" source="media/tutorial-cloud-workstation/metrics.png" alt-text="Screenshot that shows metrics for a job.":::
 
-    :::image type="content" source="media/tutorial-cloud-workstation/metrics.png" alt-text="Screenshot shows metrics for a job.":::
+1. Select the **Images** tab to view the images generated by MLflow. 
 
-1. Select the **Images** tab to view the images generated by `MLflow`. 
-
-    :::image type="content" source="media/tutorial-cloud-workstation/images.png" alt-text="Screenshot shows images for a job.":::
+    :::image type="content" source="media/tutorial-cloud-workstation/images.png" alt-text="Screenshot that shows images for a job.":::
 
 1. Go back and review the metrics and images for the other model.
 
 ## Create a Python script
 
-Now create a Python script from your notebook for model training.
-
-1. In your VS Code window, right-click on the notebook filename and select **Import Notebook to Script**.
+You'll now create a Python script from your notebook for model training.
 
-1. Use the menu **File > Save** to save this new script file. Call it **train.py**.
-1. Look through this file and delete the code you don't want in the training script. For example, keep the code for the model you wish to use, and delete code for the model you don't want.
-    * Make sure you keep the code that starts autologging (`mlflow.sklearn.autolog()`).
-    * When you run the Python script interactively (as you're doing here), you can keep the line that defines the experiment name (`mlflow.set_experiment("Develop on cloud tutorial")`). Or even give it a different name to see it as a different entry in the **Jobs** section. But when you prepare the script for a training job, that line doesn't apply and should be omitted - the job definition includes the experiment name.
-    * When you train a single model, the lines to start and end a run (`mlflow.start_run()` and `mlflow.end_run()`) are also not necessary (they'll have no effect), but can be left in if you wish.
+1. In Visual Studio Code, right-click the notebook file name and select **Import Notebook to Script**.
+1. Select **File > Save** to save the new script file. Call it **train.py**.
+1. Look through the file and delete code that you don't want in the training script. For example, keep the code for the model you want to use, and delete code for the model you don't want to use.
+    * Be sure you keep the code that starts autologging (`mlflow.sklearn.autolog()`).
+    * When you run the Python script interactively (as you're doing here), you can keep the line that defines the experiment name (`mlflow.set_experiment("Develop on cloud tutorial")`). Or you can give it a different name to see it as a different entry in the **Jobs** section. But when you prepare the script for a training job, that line doesn't apply and should be omitted: the job definition includes the experiment name.
+    * When you train a single model, the lines for starting and ending a run (`mlflow.start_run()` and `mlflow.end_run()`) are not necessary (they have no effect), but you can can leave them in.
 
 1. When you're finished with your edits, save the file.
 
 You now have a Python script to use for training your preferred model.
 
 ## Run the Python script
 
-For now, you're running this code on your compute instance, which is your Azure Machine Learning development environment. [Tutorial: Train a model](tutorial-train-model.md) shows you how to run a training script in a more scalable way on more powerful compute resources. 
+For now, you're running this code on your compute instance, which is your Azure Machine Learning development environment. [Tutorial: Train a model](tutorial-train-model.md) shows how to run a training script in a more scalable way on more powerful compute resources. 
 
-1. Select the environment you created earlier in this tutorial as your Python version (workstations_env). In the lower right corner of the notebook, you'll see the environment name. Select it, then select the environment in the middle of the screen.
+1. Select the environment you created earlier in this tutorial as your Python version (workstations_env). In the lower right corner of the notebook, you'll see the environment name. Select it, and then select the environment in the middle of the page.
 
-    :::image type="content" source="media/tutorial-cloud-workstation/select-python.png" alt-text="Screenshot shows selecting the new environment.":::
+    :::image type="content" source="media/tutorial-cloud-workstation/select-python.png" alt-text="Screenshot that shows selecting the new environment." lightbox="media/tutorial-cloud-workstation/select-python.png":::
 
-1. Now run the Python script. Use the **Run Python File** tool on the top right.
+1. Run the Python script by selecting the run button in the upper-right corner of the screen.
 
-    :::image type="content" source="media/tutorial-cloud-workstation/run-python.png" alt-text="Screenshot shows the Run Python File tool at the top right of the screen.":::
+    :::image type="content" source="media/tutorial-cloud-workstation/run-python.png" alt-text="Screenshot that shows the Run button." lightbox="media/tutorial-cloud-workstation/run-python.png":::
 
 > [!NOTE]
-> You can ignore the mlflow warnings. You'll still get all the metrics and images from autologging.
+> You can ignore the MLflow warnings. You'll still get all the metrics and images from autologging.
 
-## Examine script results
+## Examine the script results
 
-Go back to **Jobs** in your workspace in Azure Machine Learning studio to see the results of your training script. Keep in mind that the training data changes with each split, so the results differ between runs as well.
+Go back to **Jobs** in your workspace in Azure Machine Learning studio to see the results of your training script. Keep in mind that the training data changes with each split, so the results differ between runs.
 
 ## Clean up resources
 
-If you plan to continue now to other tutorials, skip to [Next steps](#next-steps).
+If you plan to continue on to other tutorials, skip to [Next steps](#next-steps).
 
 ### Stop compute instance
 
 If you're not going to use it now, stop the compute instance:
 
 1. In the studio, in the left pane, select **Compute**.
-1. In the top tabs, select **Compute instances**
-1. Select the compute instance in the list.
-1. On the top toolbar, select **Stop**.
+1. At the top of the page, select **Compute instances**.
+1. In the list, select the compute instance.
+1. At the top of the page, select **Stop**.
 
 ### Delete all resources
 
 [!INCLUDE [aml-delete-resource-group](includes/aml-delete-resource-group.md)]
 
 ## Next steps
 
-Learn more about:
+See these resources to learn more:
 
 * [From artifacts to models in MLflow](concept-mlflow-models.md)
 * [Using Git with Azure Machine Learning](concept-train-model-git-integration.md)
 * [Running Jupyter notebooks in your workspace](how-to-run-jupyter-notebooks.md)
 * [Working with a compute instance terminal in your workspace](how-to-access-terminal.md)
 * [Manage notebook and terminal sessions](how-to-manage-compute-sessions.md)
 
-This tutorial showed you the early steps of creating a model, prototyping on the same machine where the code resides. For your production training, learn how to use that training script on more powerful remote compute resources:
+This tutorial shows the early steps of creating a model, prototyping on the same machine where the code resides. For your production training, learn how to use that training script on more powerful remote compute resources:
 
 > [!div class="nextstepaction"]
 > [Train a model](tutorial-train-model.md)