Add bring your own storage to TensorFlow tutorial

Author: anthonychu

articles/azure-functions/functions-machine-learning-tensorflow.md

@@ -149,8 +149,10 @@ In Azure Functions, a function project is a container for one or more individual
 
 To modify the `classify` function to classify an image based on its contents, you use a pre-built TensorFlow model that was trained with and exported from Azure Custom Vision Service. The model, which is contained in the *resources* folder of the sample you cloned earlier, classifies an image based on whether it contains a dog or a cat. You then add some helper code and dependencies to your project.
 
+To build your own model using the free tier of the Custom Vision Service, follow the instructions in the [sample project repository](https://github.com/Azure-Samples/functions-python-tensorflow-tutorial/blob/master/train-custom-vision-model.md).
+
 > [!TIP]
-> If you want to build your own model using the free tier of the Custom Vision Service, follow the instructions in the [sample project repository](https://github.com/Azure-Samples/functions-python-tensorflow-tutorial/blob/master/train-custom-vision-model.md).
+> If you want to store your TensorFlow model separately instead of deploying it together with the function app, see [Mount a file share to a Python function app using Azure CLI](./scripts/functions-cli-mount-files-storage-linux.md).
 
 1. In the *start* folder, run following command to copy the model files into the *classify* folder. Be sure to include `\*` in the command.