vnet clarity

Author: nibaccam

articles/machine-learning/how-to-create-register-datasets.md

@@ -94,7 +94,7 @@ datastore_paths = [(datastore, 'ather/2018/11.csv'),
 weather_ds = Dataset.Tabular.from_delimited_files(path=datastore_paths)
 ```
 
-By default, when you create a TabularDataset, column data types are inferred automatically. If the inferred types don't match your expectations, you can specify column types by using the following code. If your storage is behind a virtual network or firewall, include the parameters `validate=False` and `infer_column_types=False` in your `from_delimited_files()` method. You can also [learn more about supported data types](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.datatype?view=azure-ml-py).
+By default, when you create a TabularDataset, column data types are inferred automatically. If the inferred types don't match your expectations, you can specify column types by using the following code. If your storage is behind a virtual network or firewall, include the parameters `validate=False` and `infer_column_types=False` in your `from_delimited_files()` method. This bypasses the initial validation check and ensures that you can create your dataset from these secure files. You can also [learn more about supported data types](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.datatype?view=azure-ml-py).
 
 > [!NOTE] 
 >The parameter `infer_column_type` is only applicable for datasets created from delimited files. 
@@ -150,7 +150,7 @@ data_slice = dataset.time_recent(timedelta(weeks=1, days=1))
 
 #### Create a FileDataset
 
-Use the [`from_files()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.filedatasetfactory?view=azure-ml-py#from-files-path--validate-true-) method on the `FileDatasetFactory` class to load files in any format and to create an unregistered FileDataset. If your storage is behind a a virtual network or firewall, set the parameter `validate =False` in your `from_files()` method.
+Use the [`from_files()`](https://docs.microsoft.com/python/api/azureml-core/azureml.data.dataset_factory.filedatasetfactory?view=azure-ml-py#from-files-path--validate-true-) method on the `FileDatasetFactory` class to load files in any format and to create an unregistered FileDataset. If your storage is behind a a virtual network or firewall, set the parameter `validate =False` in your `from_files()` method. This bypasses the initial validation step, and ensures that you can create your dataset from these secure files.
 
 ```Python
 # create a FileDataset pointing to files in 'animals' folder and its subfolders recursively