Acrolinx pass

Author: sdgilley

articles/machine-learning/azure-ml-glossary.md

@@ -63,15 +63,15 @@ Azure Machine Learning datastores securely keep the connection information to yo
 
 ## Environment
 
-Azure Machine Learning environments are an encapsulation of the environment where your machine learning task happens. They specify the software packages, environment variables, and software settings around your training and scoring scripts. The environments are managed and versioned entities within your Machine Learning workspace. Environments enable reproducible, auditable, and portable machine learning workflows across a variety of computes.
+Azure Machine Learning environments are an encapsulation of the environment where your machine learning task happens. They specify the software packages, environment variables, and software settings around your training and scoring scripts. The environments are managed and versioned entities within your Machine Learning workspace. Environments enable reproducible, auditable, and portable machine learning workflows across various computes.
 
 ### Types of environment
 
 Azure ML supports two types of environments: curated and custom.
 
 Curated environments are provided by Azure Machine Learning and are available in your workspace by default. Intended to be used as is, they contain collections of Python packages and settings to help you get started with various machine learning frameworks. These pre-created environments also allow for faster deployment time. For a full list, see the [curated environments article](resource-curated-environments.md).
 
-In custom environments, you're responsible for setting up your environment and installing packages or any other dependencies that your training or scoring script needs on the compute. Azure ML allows you to create your own environment using
+In custom environments, you're responsible for setting up your environment. Make sure to install the packages and any other dependencies that your training or scoring script needs on the compute. Azure ML allows you to create your own environment using
 
 * A docker image
 * A base docker image with a conda YAML to customize further