Merge pull request #212638 from MadhuM02/madhumaddi/dataset_ui_creation

registering dataset through UI.

Author: Maggiemouse1

articles/machine-learning/how-to-auto-train-image-models.md

@@ -110,7 +110,7 @@ If your training data is in a different format (like, pascal VOC or COCO), you c
 > The training data needs to have at least 10 images in order to be able to submit an AutoML run. 
 
 > [!Warning]
-> Creation of `MLTable` from data in JSONL format is supported using the SDK and CLI only, for this capability. Creating the `MLTable` via UI is not supported at this time. As of now, the UI doesn't recognize the StreamInfo datatype, which is the datatype used for image URLs in JSONL format.  
+> Creation of `MLTable` from data in JSONL format is supported using the SDK and CLI only, for this capability. Creating the `MLTable` via UI is not supported at this time. 
 
 
 ### JSONL schema samples

articles/machine-learning/how-to-prepare-datasets-for-automl-images.md

@@ -36,7 +36,7 @@ If your labeled training data is in a different format (like, pascal VOC or COCO
 ## Get labeled data 
 In order to train computer vision models using AutoML, you need to first get labeled training data. The images need to be uploaded to the cloud and label annotations need to be in JSONL format. You can either use the Azure ML Data Labeling tool to label your data or you could start with pre-labeled image data.
 
-## Using Azure ML Data Labeling tool to label your training data
+### Using Azure ML Data Labeling tool to label your training data
 If you don't have pre-labeled data, you can use Azure Machine Learning's [data labeling tool](how-to-create-image-labeling-projects.md) to manually label images. This tool automatically generates the data required for training in the accepted format.
 
 It helps to create, manage, and monitor data labeling tasks for 
@@ -72,6 +72,11 @@ my_training_data_input = Input(
     mode=InputOutputModes.DIRECT
 )
 ```
+
+# [Studio](#tab/Studio)
+
+Please refer to Cli/Sdk tabs for reference.
+
 ---
 
 ### Using pre-labeled training data from local machine
@@ -104,7 +109,12 @@ az ml data create -f [PATH_TO_YML_FILE] --workspace-name [YOUR_AZURE_WORKSPACE]
 
  [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
 
-[!Notebook-python[] (~/azureml-examples-v2samplesreorg/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=upload-data)]
+[!Notebook-python[] (~/azureml-examples-main/sdk/python/jobs/automl-standalone-jobs/automl-image-object-detection-task-fridge-items/automl-image-object-detection-task-fridge-items.ipynb?name=upload-data)]
+
+# [Studio](#tab/Studio)
+
+![Animation showing how to register a dataset from local files](media\how-to-prepare-datasets-for-automl-images\ui-dataset-local.gif)
+
 ---
 
 If you already have your data present in an existing datastore and want to create a data asset out of it, you can do so by providing the path to the data in the datastore, instead of providing the path of your local machine. Update the code [above](how-to-prepare-datasets-for-automl-images.md#using-pre-labeled-training-data-from-local-machine) with the following snippet.
@@ -133,12 +143,21 @@ my_data = Data(
     name="fridge-items-images-object-detection",
 )
 ```
+
+# [Studio](#tab/Studio)
+
+![Animation showing how to register a dataset from data already present in datastore](media\how-to-prepare-datasets-for-automl-images\ui-dataset-datastore.gif)
+
 ---
 
 Next, you will need to get the label annotations in JSONL format. The schema of labeled data depends on the computer vision task at hand. Refer to [schemas for JSONL files for AutoML computer vision experiments](reference-automl-images-schema.md) to learn more about the required JSONL schema for each task type.
 
 If your training data is in a different format (like, pascal VOC or COCO), [helper scripts](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/v1/python-sdk/tutorials/automl-with-azureml/image-object-detection/coco2jsonl.py) to convert the data to JSONL are available in [notebook examples](https://github.com/Azure/azureml-examples/blob/v2samplesreorg/sdk/python/jobs/automl-standalone-jobs).
 
+Once you have created jsonl file following the above steps, you can register it as a data asset using UI. Make sure you select `stream` type in schema section as shown below.
+
+![Animation showing how to register a data asset from the jsonl files](media\how-to-prepare-datasets-for-automl-images\ui-dataset-jsnol.gif)
+
 ### Using pre-labeled training data from Azure Blob storage
 If you have your labeled training data present in a container in Azure Blob storage, then you can access it directly from there by [creating a datastore referring to that container](how-to-datastore.md#create-an-azure-blob-datastore).