Update how-to-deploy-and-where.md

Author: jpe316

articles/machine-learning/service/how-to-deploy-and-where.md

@@ -112,13 +112,13 @@ The script contains two functions that load and run the model:
 
 * `run(input_data)`: This function uses the model to predict a value based on the input data. Inputs and outputs to the run typically use JSON for serialization and de-serialization. You can also work with raw binary data. You can transform the data before sending to the model, or before returning to the client.
 
-#### What is model_path?
+#### What is get_model_path?
+When you register a model, you provide a model name used for managing the model in the registry. You use this name in the get_model_path API which returns the path of the model file(s) on the local file system. **If you register a folder or a collection of files, this API returns the path to the directory which contains those files.**
+
 In Azure ML, we use a function called get_model_path to retrieve the local location of your model.
 When you register a model, you give it a name which corresponds to where the model is placed, either locally or during service deployment.
 
-When you use this function, keep in mind that it is looking for this file and local path, first in the official path for Azure ML models, then locally.
-
-The below example will return a path to a file called 'sklearn_mnist_model.pkl' (which was registered with the name 'sklearn_mnist')
+The below example will return a path to a single file called 'sklearn_mnist_model.pkl' (which was registered with the name 'sklearn_mnist')
 ```
 model_path = Model.get_model_path('sklearn_mnist')
 ```