Merge pull request #88168 from nibaccam/dataset-ui

Data | create dataset via UI

Author: v-shils

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

@@ -72,8 +72,13 @@ workspace = Workspace.from_config()
 # retrieve an existing datastore in the workspace by name
 datastore = Datastore.get(workspace, datastore_name)
 ```
+
 ### Create TabularDatasets
 
+TabularDatasets can be created via the SDK or by using the workspace landing page (preview).
+
+#### SDK 
+
 Use the `from_delimited_files()` method on `TabularDatasetFactory` class to read files in csv or tsv format, and create an unregistered TabularDataset. If you are reading from multiple files, results will be aggregated into one tabular representation.
 
 ```Python
@@ -99,7 +104,18 @@ titanic_ds.take(3).to_pandas_dataframe()
 1|2|1|1|Cumings, Mrs. John Bradley (Florence Briggs Th...|female|38.0|1|0|PC 17599|71.2833|C85|C
 2|3|1|3|Heikkinen, Miss. Laina|female|26.0|0|0|STON/O2. 3101282|7.9250||S
 
+#### Workspace landing page 
+
+Sign in to the [workspace landing page](https://ml.azure.com) to create a dataset via the web experience. Currently, the workspace landing page only supports the creation of TabularDatasets.
+
+The following animation shows how to create a dataset in the workspace landing page. 
+
+First, select **Datasets** in the **Assets** section of the left pane. Then,  select **+ Create Dataset** to choose the source of your dataset; this can either be from local files, datastore or public web urls. The **Settings and preview** and the **Schema** forms are intelligently populated based on file type. Select **Next** to review them or to further configure your dataset prior to creation. Select **Done** to complete your dataset creation. 
+
+![Create a dataset with the UI](media/how-to-create-register-datasets/create-dataset-ui.gif)
+
 ### Create FileDatasets
+
 Use the `from_files()` method on `FileDatasetFactory` class to load files in any format, and create an unregistered FileDataset.
 
 ```Python
@@ -130,14 +146,17 @@ titanic_ds = titanic_ds.register(workspace = workspace,
                                  description = 'titanic training data')
 ```
 
+>[!Note]
+> Datasets created via the workspace landing page are automatically registered to the workspace. 
+
 ## Version datasets
 
 You can register a new dataset under the same name by creating a new version. Dataset version is a way to bookmark the state of your data, so you can apply a specific version of the dataset for experimentation or future reproduction. Typical scenarios to consider versioning: 
 * When new data is available for retraining.
 * When you are applying different data preparation or feature engineering approaches.
 
 ```Python
-# create a TabularDataset from new Titanic training data
+# create a TabularDataset from Titanic training data
 web_paths = [
             'https://dprepdata.blob.core.windows.net/demo/Titanic.csv',
             'https://dprepdata.blob.core.windows.net/demo/Titanic2.csv'