acrolinx

Author: Larry Franks

articles/machine-learning/how-to-read-write-data-v2.md

@@ -68,8 +68,8 @@ Type | Input/Output | `upload` | `download` | `ro_mount` | `rw_mount` | `direct`
 
 Create a job specification YAML file (`<file-name>.yml`). Specify in the `inputs` section of the job:
 
-1. The `type`; whether the data you are pointing to is a specific file  (`uri_file`) or a folder location (`uri_folder`) or an `mltable`. 
-1. The `path` of where your data is located; the path can be any of those outlined in the [Supported Paths](#supported-paths) section. 
+1. The `type`; whether the data is a specific file  (`uri_file`) or a folder location (`uri_folder`) or an `mltable`. 
+1. The `path` of where your data is located; can be any of the paths outlined in the [Supported Paths](#supported-paths) section. 
 
 ```yaml
 $schema: https://azuremlschemas.azureedge.net/latest/commandJob.schema.json
@@ -100,8 +100,8 @@ az ml job create -f <file-name>.yml
 
 The `Input` class allows you to define:
 
-1. The `type`; whether the data you are pointing to is a specific file  (`uri_file`) or a folder location (`uri_folder`) or an `mltable`. 
-1. The `path` of where your data is located; the path can be any of those outlined in the [Supported Paths](#supported-paths) section. 
+1. The `type`; whether the data is a specific file  (`uri_file`) or a folder location (`uri_folder`) or an `mltable`. 
+1. The `path` of where your data is located; can be any of the paths outlined in the [Supported Paths](#supported-paths) section. 
 
 ```python
 from azure.ai.ml import command