adding cli env json schema

Author: Trevor Bye

articles/machine-learning/reference-azure-machine-learning-cli.md

@@ -259,6 +259,65 @@ The following commands demonstrate how to create, register, and list Azure Machi
 
     For more information, see [az ml environment download](https://docs.microsoft.com/cli/azure/ext/azure-cli-ml/ml/environment?view=azure-cli-latest#ext-azure-cli-ml-az-ml-environment-download).
 
+### Environment configuration schema
+
+If you used the `az ml environment scaffold` command, it generates a template `azureml_environment.json` file that can be modified and used to create custom environment configurations with the CLI. The top level object loosely maps to the [`Environment`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment(class)?view=azure-ml-py) class in the Python SDK. 
+
+```json
+{
+    "name": "testenv",
+    "version": null,
+    "environmentVariables": {
+        "EXAMPLE_ENV_VAR": "EXAMPLE_VALUE"
+    },
+    "python": {
+        "userManagedDependencies": false,
+        "interpreterPath": "python",
+        "condaDependenciesFile": null,
+        "baseCondaEnvironment": null
+    },
+    "docker": {
+        "enabled": false,
+        "baseImage": "mcr.microsoft.com/azureml/base:intelmpi2018.3-ubuntu16.04",
+        "baseDockerfile": null,
+        "sharedVolumes": true,
+        "shmSize": "2g",
+        "arguments": [],
+        "baseImageRegistry": {
+            "address": null,
+            "username": null,
+            "password": null
+        }
+    },
+    "spark": {
+        "repositories": [],
+        "packages": [],
+        "precachePackages": true
+    },
+    "databricks": {
+        "mavenLibraries": [],
+        "pypiLibraries": [],
+        "rcranLibraries": [],
+        "jarLibraries": [],
+        "eggLibraries": []
+    },
+    "inferencingStackVersion": null
+}
+```
+
+The following table details each top-level field in the JSON file, it's type, and a description. If an object type is linked to a class from the Python SDK, there is a loose 1:1 match between each JSON field and the public variable name in the Python class. For example, the `environmentVariables` field maps to the `environment_variables` variable in the [`Environment`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment(class)?view=azure-ml-py) class.
+
+| JSON field | Type | Description |
+|---|---|---|
+| `name` | `string` | Name of the environment. Do not start name with **Microsoft** or **AzureML**. |
+| `version` | `string` | Version of the environment. |
+| `environmentVariables` | `{string: string}` | A hash-map of environment variable names and values. |
+| `python` | [`PythonSection`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.pythonsection?view=azure-ml-py) | Object that defines the Python environment and interpreter to use on target compute resource. |
+| `docker` | [`DockerSection`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.dockersection?view=azure-ml-py) | Defines settings to customize the Docker image built to the environment's specifications. |
+| `spark` | [`SparkSection`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.environment.sparksection?view=azure-ml-py) | The section configures Spark settings. It is only used when framework is set to PySpark. |
+| `databricks` | [`DatabricksSection`](https://docs.microsoft.com/python/api/azureml-core/azureml.core.databricks.databrickssection?view=azure-ml-py) | Configures Databricks library dependencies. |
+| `inferencingStackVersion` | `string` | Specifies the inferencing stack version added to the image. To avoid adding an inferencing stack, leave this field `null`. Valid value: "latest". |
+
 ## ML pipeline management
 
 The following commands demonstrate how to work with machine learning pipelines: