Merge pull request #106766 from Blackmist/feedback

incorporating feedback

Author: PRMerger16

articles/machine-learning/concept-azure-machine-learning-architecture.md

@@ -132,7 +132,7 @@ When you develop your solution, use the Azure Machine Learning Python SDK in you
 
 You use machine learning pipelines to create and manage workflows that stitch together machine learning phases. For example, a pipeline might include data preparation, model training, model deployment, and inference/scoring phases. Each phase can encompass multiple steps, each of which can run unattended in various compute targets. 
 
-Pipeline steps are reusable, and can be run without rerunning subsequent steps if the output of that step hasn't changed. For example, you can retrain a model without rerunning costly data preparation steps if the data hasn't changed. Pipelines also allow data scientists to collaborate while working on separate areas of a machine learning workflow.
+Pipeline steps are reusable, and can be run without rerunning the previous steps if the output of those steps hasn't changed. For example, you can retrain a model without rerunning costly data preparation steps if the data hasn't changed. Pipelines also allow data scientists to collaborate while working on separate areas of a machine learning workflow.
 
 For more information about machine learning pipelines with this service, see [Pipelines and Azure Machine Learning](concept-ml-pipelines.md).
 

articles/machine-learning/how-to-deploy-custom-docker-image.md

@@ -150,6 +150,9 @@ The steps in this section walk-through creating a custom Docker image in your Az
     az acr build --image myimage:v1 --registry <registry_name> --file Dockerfile .
     ```
 
+    > [!TIP]
+    > In this example, a tag of `:v1` is applied to the image. If no tag is provided, a tag of `:latest` is applied.
+
     During the build process, information is streamed to back to the command line. If the build is successful, you receive a message similar to the following text:
 
     ```text
@@ -165,6 +168,10 @@ For more information on uploading existing images to an Azure Container Registry
 To use a custom image, you need the following information:
 
 * The __image name__. For example, `mcr.microsoft.com/azureml/o16n-sample-user-base/ubuntu-miniconda` is the path to a basic Docker Image provided by Microsoft.
+
+    > [!IMPORTANT]
+    > For custom images that you've created, be sure to include any tags that were used with the image. For example, if your image was created with a specific tag, such as `:v1`. If you did not use a specific tag when creating the image, a tag of `:latest` was applied.
+
 * If the image is in a __private repository__, you need the following information:
 
     * The registry __address__. For example, `myregistry.azureecr.io`.