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Author: nibaccam

articles/machine-learning/how-to-train-with-datasets.md

@@ -58,13 +58,13 @@ dataset = run.input_datasets['titanic']
 df = dataset.to_pandas_dataframe()
 ```
 
-If you need to load the prepared data into a new dataset from an in memory pandas dataframe, write the data to a local file, like a parquet, and create a new dataset from that file. You can also create datasets from local files or paths in datastores. Learn more about [how to create datasets](https://aka.ms/azureml/howto/createdatasets).
+If you need to load the prepared data into a new dataset from an in memory pandas dataframe, write the data to a local file, like a parquet, and create a new dataset from that file. You can also create datasets from local files or paths in datastores. Learn more about [how to create datasets](how-to-create-register-datasets.md).
 
 ## Use datasets directly in training scripts
 
-If you have structured data, create a TabularDataset and use it directly in your training script for your local or remote experiment.
+If you have structured data not yet registered as a dataset, create a TabularDataset and use it directly in your training script for your local or remote experiment.
 
-In this example, you create a [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) and use it as a direct input to your `estimator` object for training. 
+In this example, you create an unregistered [TabularDataset](https://docs.microsoft.com/python/api/azureml-core/azureml.data.tabulardataset?view=azure-ml-py) and use it as a direct input to your `estimator` object for training. If you want to reuse this TabularDataset with other experiments in your workspace, see [how to register datasets to your workspace](how-to-create-register-datasets.md#register-datasets).
 
 ### Create a TabularDataset
 
@@ -77,7 +77,7 @@ web_path ='https://dprepdata.blob.core.windows.net/demo/Titanic.csv'
 titanic_ds = Dataset.Tabular.from_delimited_files(path=web_path)
 ```
 
-TabularDataset objects provide the ability to load the data into a pandas or spark DataFrame so that you can work with familiar data preparation and training libraries without having to leave your notebook. To leverage this capability, see [access and explore input datasets](#access-and-explore-input-datasets).
+TabularDataset objects provide the ability to load the data in your TabularDataset into a pandas or spark DataFrame so that you can work with familiar data preparation and training libraries without having to leave your notebook. To leverage this capability, see [access and explore input datasets](#access-and-explore-input-datasets).
 
 ### Configure the estimator