Merge pull request #268271 from cdpark/azureml-manage-ositanachi

User Story 226300: Q&M: AzureML Freshness updates - Manage environments

Author: denrea

articles/machine-learning/how-to-manage-environments-in-studio.md

@@ -8,14 +8,14 @@ ms.subservice: core
 author: ositanachi
 ms.author:  osiotugo
 ms.reviewer: larryfr
-ms.date: 01/30/2023
+ms.date: 03/05/2024
 ms.topic: how-to
 ms.custom:
 ---
 
 # Manage software environments in Azure Machine Learning studio
 
-In this article, learn how to create and manage Azure Machine Learning [environments](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) in the Azure Machine Learning studio. Use the environments to track and reproduce your projects' software dependencies as they evolve.
+This article explains how to create and manage [Azure Machine Learning environments](/python/api/azure-ai-ml/azure.ai.ml.entities.environment) in the Azure Machine Learning studio. Use the environments to track and reproduce your projects' software dependencies as they evolve.
 
 The examples in this article show how to:
 
@@ -24,60 +24,66 @@ The examples in this article show how to:
 * Edit an existing environment specification and its properties.
 * Rebuild an environment and view image build logs.
 
-For a high-level overview of how environments work in Azure Machine Learning, see [What are ML environments?](concept-environments.md) For information, see [How to set up a development environment for Azure Machine Learning](how-to-configure-environment.md).
+For a high-level overview of environments, see [What are Azure Machine Learning environments?](concept-environments.md) For more information, see [How to set up a development environment for Azure Machine Learning](how-to-configure-environment.md).
 
 ## Prerequisites
 
-* An Azure subscription. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free/) before you begin.
+* An Azure subscription. If you don't have an Azure subscription, create a [free account](https://azure.microsoft.com/free).
 * An [Azure Machine Learning workspace](quickstart-create-resources.md).
 
 ## Browse curated environments
 
-Curated environments contain collections of Python packages and are available in your workspace by default. These environments are backed by cached Docker images, which reduce the job preparation cost and support training and inferencing scenarios. 
+Curated environments contain collections of Python packages and are available in your workspace by default. These environments are backed by cached Docker images, which reduce the job preparation cost and support training and inferencing scenarios.
 
-Click on an environment to see detailed information about its contents. For more information, see [Azure Machine Learning curated environments](resource-curated-environments.md). 
+Select an environment to see detailed information about its contents. For more information, see [Azure Machine Learning curated environments](resource-curated-environments.md).
 
 ## Create an environment
 
 To create an environment:
 1. Open your workspace in [Azure Machine Learning studio](https://ml.azure.com).
 1. On the left side, select **Environments**.
-1. Select the **Custom environments** tab. 
-1. Select the **Create** button. 
+1. Select the **Custom environments** tab.
+1. Select the **Create** button.
 
-Create an environment by selecting one of the following options:
-* Create a new docker [context](https://docs.docker.com/engine/reference/commandline/build/)
-* Start from an existing environment
-* Upload existing docker context
-* Use existing docker image with optional conda file
+Select one of the following options:
+* Create a new docker [context](https://docs.docker.com/engine/reference/commandline/build/).
+* Start from an existing environment.
+* Upload an existing docker context.
+* Use existing docker image with optional conda file.
 
-:::image type="content" source="media/how-to-manage-environments-in-studio/create-page.png" alt-text="Environment creation wizard":::
+:::image type="content" source="media/how-to-manage-environments-in-studio/create-page.png" alt-text="Screenshot of the environment creation wizard.":::
 
-You can customize the configuration file, add tags and descriptions, and review the properties before creating the entity. 
+You can customize the configuration file, add tags and descriptions, and review the properties before creating the entity.
 
-If a new environment is given the same name as an existing environment in the workspace, a new version of the existing one will be created.
+If a new environment is given the same name as an existing environment in the workspace, a new version of the existing one is created.
 
 ## View and edit environment details
 
-Once an environment has been created, view its details by clicking on the name. Use the dropdown menu to select different versions of the environment. Here you can view metadata and the contents of the environment through its various dependencies.
+Once an environment has been created, view its details by selecting the name. Use the dropdown menu to select different versions of the environment. Here you can view metadata and the contents of the environment through its various dependencies.
 
-Click on the pencil icons to edit tags, descriptions, configuration files under the **Context** tab.
+Select the pencil icons to edit tags, descriptions, configuration files under the **Context** tab.
 
-Keep in mind that any changes to the Docker or Conda sections will create a new version of the environment. 
+Keep in mind that any changes to the Docker or Conda sections create a new version of the environment.
 
-:::image type="content" source="media/how-to-manage-environments-in-studio/details-page.png" alt-text="Environments details page":::
+:::image type="content" source="media/how-to-manage-environments-in-studio/details-page.png" alt-text="Screenshot of the environment details page.":::
 
 ## View logs
 
-Click on the **Build log** tab within the details page to view the logs of an environment version and the environment log analysis. Environment log analysis is a feature that provides insight and relevant troubleshooting documentation to explain environment definition issues or image build failures.
-* Build log contains the bare output from an Azure Container Registry (ACR) task or an Image Build Compute job.
+Select the **Build log** tab on the details page to view the logs of an environment version and the environment log analysis. Environment log analysis is a feature that provides insight and relevant troubleshooting documentation to explain environment definition issues or image build failures.
+
+* The build log contains the bare output from an Azure Container Registry (ACR) task or an Image Build Compute job.
 * Image build analysis is an analysis of the build log used to see the cause of the image build failure.
 * Environment definition analysis provides information about the environment definition if it goes against best practices for reproducibility, supportability, or security.
 
-For an overview of common build failures, see [How to troubleshoot for environments](https://aka.ms/azureml/environment/troubleshooting-guide).
+For an overview of common build failures, see [Troubleshooting environment issues](https://aka.ms/azureml/environment/troubleshooting-guide).
 
 If you have feedback on the environment log analysis, file a [GitHub issue](https://aka.ms/azureml/environment/log-analysis-feedback).
 
 ## Rebuild an environment
 
-In the details page, click on the **rebuild** button to rebuild the environment. Any unpinned package versions in your configuration files may be updated to the most recent version with this action. 
+On the details page, select the **Rebuild** button to rebuild the environment. Any unpinned package versions in your configuration files might be updated to the most recent version with this action.
+
+## Next step
+
+> [!div class="nextstepaction"]
+> [How to create and manage files in your workspace](how-to-manage-files.md)