Merge pull request #100833 from itechedit/concept-environments

edit pass: concept-environments

Author: ShawnJackson

articles/machine-learning/concept-environments.md

@@ -1,7 +1,7 @@
 ---
-title: 'What are ML Environments'
+title: About Azure Machine Learning environments
 titleSuffix: Azure Machine Learning
-description: In this article, learn the advantages of machine learning environments, that enable reproducible, auditable, and portable machine learning dependency definitions across different compute targets.
+description: In this article, learn the advantages of machine learning environments, which enable reproducible, auditable, and portable machine learning dependency definitions across a variety of compute targets.
 services: machine-learning
 ms.service: machine-learning
 ms.subservice: core
@@ -14,44 +14,47 @@ ms.date: 01/06/2020
 # What are Azure Machine Learning environments?
 [!INCLUDE [applies-to-skus](../../includes/aml-applies-to-basic-enterprise-sku.md)]
 
-Environments specify the Python packages, environment variables, and software settings around your training and scoring scripts, and run times (Python, Spark, or Docker). They are managed and versioned entities within your Azure Machine Learning workspace that enable reproducible, auditable, and portable machine learning workflows across different compute targets.
+Azure Machine Learning environments specify the Python packages, environment variables, and software settings around your training and scoring scripts. They also specify run times (Python, Spark, or Docker). They are managed and versioned entities within your Machine Learning workspace that enable reproducible, auditable, and portable machine learning workflows across a variety of compute targets.
 
-You can use an environment object on your local compute to develop your training script, reuse that same environment on Azure Machine Learning Compute for model training at scale, and even deploy your model with that same environment.
+You can use an `Environment` object on your local compute to:
+* Develop your training script.
+* Reuse the same environment on Azure Machine Learning Compute for model training at scale.
+* Deploy your model with that same environment.
 
-The following illustrates that the same environment object can be used in both your run configuration for training and in your inference and deployment configuration for web service deployments.
+The following diagram illustrates how you can use a single `Environment` object in both your run configuration, for training, and your inference and deployment configuration, for web service deployments.
 
-![Diagram of environment in machine learning workflow](./media/concept-environments/ml-environment.png)
+![Diagram of an environment in machine learning workflow](./media/concept-environments/ml-environment.png)
 
 ## Types of environments
 
-Environments can broadly be divided into three categories: **curated**, **user-managed** and **system-managed**.
+Environments can broadly be divided into three categories: *curated*, *user-managed*, and *system-managed*.
 
-Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They contain collections of Python packages and settings to help you get started different machine learning frameworks. 
+Curated environments are provided by Azure Machine Learning and are available in your workspace by default. They contain collections of Python packages and settings to help you get started with various machine learning frameworks. 
 
-For a user-managed environment, you're responsible for setting up your environment and installing every package your training script needs on the compute target. Conda will not check your environment or install anything for you. Please note that if you are defining your own environment, you must list `azureml-defaults` with version `>= 1.0.45` as a pip dependency. This package contains the functionality needed to host the model as a web service.
+In user-managed environments, you're responsible for setting up your environment and installing every package that your training script needs on the compute target. Conda doesn't check your environment or install anything for you. If you're defining your own environment, you must list `azureml-defaults` with version `>= 1.0.45` as a pip dependency. This package contains the functionality that's needed to host the model as a web service.
 
-System-managed environments are used when you want [Conda](https://conda.io/docs/) to manage the Python environment and the script dependencies for you. The service assumes this type of environment by default, due to its usefulness on remote compute targets that are not manually configurable.
+You use system-managed environments when you want [Conda](https://conda.io/docs/) to manage the Python environment and the script dependencies for you. The service assumes this type of environment by default, because of its usefulness on remote compute targets that are not manually configurable.
 
-## Creating and managing environments
+## Create and manage environments
 
-Environments can be created by:
+You can create environments by:
 
-* Defining new `Environment` objects, either using a curated environment or by defining your own dependencies
-* Using existing `Environment` objects from your workspace. This allows for consistency and reproducibility with your dependencies
+* Defining new `Environment` objects, either by using a curated environment or by defining your own dependencies.
+* Using existing `Environment` objects from your workspace. This approach allows for consistency and reproducibility with your dependencies.
 * Importing from an existing Anaconda environment definition.
 * Using the Azure Machine Learning CLI
 
-See the [how-to](how-to-use-environments.md#create-an-environment) for specific code examples. Environments are also easily managed through your workspace and include the following functionality:
+For specific code samples, see the "Create an environment" section of [Reuse environments for training and deployment](how-to-use-environments.md#create-an-environment). Environments are also easily managed through your workspace. They include the following functionality:
 
-* Environments are automatically registered to your workspace when you submit an experiment. They can also be manually registered
-* Fetch environments from your workspace, and use them for training, deployment, or make edits to the environment definition
-* Versioning allows you to see changes to your environments over time, and ensures reproducibility
-* Build Docker images automatically from your environments
+* Environments are automatically registered to your workspace when you submit an experiment. They can also be manually registered.
+* You can fetch environments from your workspace to use for training or deployment, or to make edits to the environment definition.
+* With versioning, you can see changes to your environments over time, which ensures reproducibility.
+* You can build Docker images automatically from your environments.
 
-See the [manage environments](how-to-use-environments.md#manage-environments) section of the how-to for code samples.
+For code samples, see the "Manage environments" section of [Reuse environments for training and deployment](how-to-use-environments.md#manage-environments).
 
 ## Next steps
 
-* Learn how-to [create and use environments](how-to-use-environments.md) in Azure Machine Learning
-* See the Python SDK reference docs for the [environment class](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment(class)?view=azure-ml-py).
-* See the R SDK reference docs for [environments](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-environments).
+* Learn how to [create and use environments](how-to-use-environments.md) in Azure Machine Learning.
+* See the Python SDK reference documentation for the [environment class](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment(class)?view=azure-ml-py).
+* See the R SDK reference documentation for [environments](https://azure.github.io/azureml-sdk-for-r/reference/index.html#section-environments).