Made additional changes to the documentation.

Author: dhanvia

articles/machine-learning/how-to-deploy-custom-container.md

@@ -340,7 +340,7 @@ You can optionally configure your `model_mount_path` value. By adjusting this se
 > The `model_mount_path` value must be a valid absolute path in Linux (the OS of the container image).
 
 > [!IMPORTANT]
-> For BYOC scenarios, where a custom `model_mount_path` is to be configured on an online deployment, the [The inference_config parameter](#the-inference_config-parameter) parameter is also required to be set in the Environment, for the environment to be recognized as a custom environment. For such scenarios, please use Azure CLI or Python SDK, as there are known limitations in configuring `inference_config` parameter when creating custom environment from Azure Portal.
+> For BYOC scenarios, where a custom `model_mount_path` is to be configured on an online deployment, the [The inference_config parameter](#the-inference_config-parameter) parameter is also required to be set in the custom environment created for the model, for the environment to be recognized as a custom environment. For such scenarios, please use Azure CLI or Python SDK, as there are known limitations in configuring `inference_config` parameter when creating custom environment from Azure Portal.
 
 When you change the value of `model_mount_path`, you also need to update the `MODEL_BASE_PATH` environment variable. Set `MODEL_BASE_PATH` to the same value as `model_mount_path` to avoid a failed deployment due to an error about the base path not being found.