added new CLI snippets

Author: AbeOmor

articles/machine-learning/how-to-manage-models.md

@@ -357,7 +357,7 @@ Create a job specification YAML file (`<file-name>.yml`). Specify in the `inputs
 1. The `type`; whether the model is a `mlflow_model`,`custom_model` or `triton_model`. 
 1. The `path` of where your data is located; can be any of the paths outlined in the [Supported Paths](#supported-paths) section. 
 
-<!-- :::code language="yaml" source="~/azureml-examples-main/CLI/assets/model/job-model-as-input.yml"::: >
+:::code language="yaml" source="~/azureml-examples-main/CLI/jobs/basics/hello-model-as-input.yml":::
 
 Next, run in the CLI
 
@@ -421,7 +421,7 @@ In your job you can write model to your cloud-based storage using *outputs*.
 
 Create a job specification YAML file (`<file-name>.yml`), with the `outputs` section populated with the type and path of where you would like to write your data to:
 
-<!-- :::code language="yaml" source="~/azureml-examples-main/CLI/assets/model/job-model-as-output.yml"::: -->
+:::code language="yaml" source="~/azureml-examples-main/CLI/jobs/basics/hello-model-as-output.yml":::
 
 Next create a job using the CLI: