data asset using jsnol

Author: MadhuM02

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

@@ -47,12 +47,39 @@ It helps to create, manage, and monitor data labeling tasks for
 
 If you already have a data labeling project and you want to use that data, you can [export your labeled data as an Azure ML Dataset](how-to-create-image-labeling-projects.md#export-the-labels) and then access the dataset under 'Datasets' tab in Azure ML Studio. This exported dataset can then be passed as an input using `azureml:<tabulardataset_name>:<version>` format. Here is an example on how to pass existing dataset as input for training computer vision models.
 
+# [Azure CLI](#tab/cli)
+
+[!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
+
+```yaml
+training_data:
+  path: azureml:odFridgeObjectsTrainingDataset:1
+  type: mltable
+  mode: direct
+```
+
+# [Python SDK](#tab/python)
+
+ [!INCLUDE [sdk v2](../../includes/machine-learning-sdk-v2.md)]
+
+```python
+from azure.ai.ml.constants import AssetTypes, InputOutputModes
+from azure.ai.ml import Input
+
+# Training MLTable with v1 TabularDataset
+my_training_data_input = Input(
+    type=AssetTypes.MLTABLE, path="azureml:odFridgeObjectsTrainingDataset:1",
+    mode=InputOutputModes.DIRECT
+)
+```
+---
+
 ### Using pre-labeled training data from local machine
-If you have previously labeled data that you would like to use to train your model, you will first need to upload the images to the default Azure Blob Storage of your Azure ML Workspace and register it as a data asset. 
+If you have previously labeled data that you would like to use to train your model, you will first need to upload the images to the default Azure Blob Storage of your Azure ML Workspace and register it as a [data asset](how-to-create-data-assets.md). 
 
-Below scripts uploads the image data on your local machine at path "./data/odFridgeObjects" to datastore in Azure Blob Storage. Thereafter, it creates a new data asset with the name "fridge-items-images-object-detection" in your Azure ML Workspace. 
+The following script uploads the image data on your local machine at path "./data/odFridgeObjects" to datastore in Azure Blob Storage. It then creates a new data asset with the name "fridge-items-images-object-detection" in your Azure ML Workspace. 
 
-If there already exists a data asset with name "fridge-items-images-object-detection" in your Azure ML Workspace, then it'll update its version number of data asset and make it point to new location in datastore in Azure Blob Storage where we uploaded the image data.
+If there already exists a data asset with the name "fridge-items-images-object-detection" in your Azure ML Workspace, it will update the version number of the data asset and point it to the new location where the image data uploaded.
 
 # [Azure CLI](#tab/cli)
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
@@ -85,7 +112,7 @@ az ml data create -f [PATH_TO_YML_FILE] --workspace-name [YOUR_AZURE_WORKSPACE]
 
 ---
 
-If you already have your data present in an existing datastore and want to create data asset out of it, you can do so by providing path to the data in datastore as shown below, instead of providing path on your local machine.
+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.
 
 # [Azure CLI](#tab/cli)
 [!INCLUDE [cli v2](../../includes/machine-learning-cli-v2.md)]
@@ -96,7 +123,7 @@ Create a .yml file with the following configuration.
 $schema: https://azuremlschemas.azureedge.net/latest/data.schema.json
 name: fridge-items-images-object-detection
 description: Fridge-items images Object detection
-path: azureml://subscriptions/<fmy-subscription-id>/resourcegroups/<my-resource-group>/workspaces/<my-workspace>/datastores/<my-datastore>/paths/<path_to_image_data_folder>
+path: azureml://subscriptions/<my-subscription-id>/resourcegroups/<my-resource-group>/workspaces/<my-workspace>/datastores/<my-datastore>/paths/<path_to_image_data_folder>
 type: uri_folder
 ```
 
@@ -122,11 +149,12 @@ Next, you will need to get the label annotations in JSONL format. The schema of
 
 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 which is described in below animation.
 
-### Using pre-labeled training data from Azure Blob storage
-If you have your labelled 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). Once you have created a datastore in AML workspace, linked to a existing container in blob, you'll have to update authentication details for that datastore. You'll have to select subscription id, resource group and provide either Account Key or SAS token.
+![Animation showing how to register a data asset from the jsonl files](media\how-to-prepare-datasets-for-automl-images\ui-dataset-jsnol.gif)
 
-![Update Authentication for Datastore.](media/how-to-prepare-datasets-for-automl-images/update-datastore-authentication.png)
+### Using pre-labeled training data from Azure Blob storage
+If you have your labelled 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). 
 
 ## Create MLTable