added cli info to env articles

Author: Trevor Bye

articles/machine-learning/concept-environments.md

@@ -39,6 +39,7 @@ Environments can be created 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
 * 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:
 

articles/machine-learning/how-to-use-environments.md

@@ -344,6 +344,34 @@ service = Model.deploy(
 
 This [example notebook](https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/training/using-environments) expands upon concepts and methods demonstrated in this article.
 
+## Create and manage environments with the CLI
+
+The [Azure Machine Learning CLI](reference-azure-machine-learning-cli.md) mirrors the majority of the functionality of the Python SDK, and can be used for environment creation and management. The following commands demonstrate basic functionality.
+
+The following command scaffolds the files for a default environment definition in the specified directory. These files are JSON files that are similar in function to the corresponding class in the SDK, and can be used to create new environments with custom settings. 
+
+```azurecli-interactive
+az ml environment scaffold -n myenv -d myenvdir
+```
+
+Run the following command to register an environment from a specified directory.
+
+```azurecli-interactive
+az ml environment register -d myenvdir
+```
+
+Running the following command will list all registered environments.
+
+```azurecli-interactive
+az ml environment list
+```
+
+Download a registered environment with the following command.
+
+```azurecli-interactive
+az ml environment download -n myenv -d downloaddir
+```
+
 ## Next steps
 
 * [Tutorial: Train a model](tutorial-train-models-with-aml.md) uses a managed compute target to  train a model.