Merge pull request #99859 from sdgilley/sdg-labeling-tutorial

labeling tutorial

Author: American-Dipper

articles/machine-learning/how-to-create-labeling-projects.md

@@ -16,14 +16,14 @@ ms.date: 03/01/2020
 
 Labeling voluminous data in machine learning projects is often a headache. Projects that have a computer-vision component, such as image classification or object detection, generally require labels for thousands of images.
 
-[Azure Machine Learning](https://ml.azure.com/) gives you a central place to create, manage, and monitor labeling projects. Use it to coordinate data, labels, and team members to efficiently manage labeling tasks. Machine Learning supports image classification, either multi-label or multi-class, and object identification with bounded boxes.
+[Azure Machine Learning](https://ml.azure.com/) gives you a central place to create, manage, and monitor labeling projects (public preview). Use it to coordinate data, labels, and team members to efficiently manage labeling tasks. Machine Learning supports image classification, either multi-label or multi-class, and object identification with bounded boxes.
 
 Machine Learning tracks progress and maintains the queue of incomplete labeling tasks. Labelers don't need an Azure account to participate. After they are authenticated with your Microsoft account or [Azure Active Directory](https://docs.microsoft.com/azure/active-directory/active-directory-whatis), they can do as much labeling as their time allows.
 
 You start and stop the project, add and remove labelers and teams, and monitor the labeling progress. You can export labeled data in COCO format or as an Azure Machine Learning dataset.
 
 > [!Important]
-> Only image classification and object identification labeling projects are currently supported. Additionally, the data images must be available in an Azure blob datastore. (If you do not have an existing datastore, you may upload images during project creation.) 
+> Only image classification and object identification labeling projects are currently supported. Additionally, the data images must be available in an Azure blob datastore. (If you do not have an existing datastore, you may upload images during project creation.)
 
 In this article, you'll learn how to:
 
@@ -37,6 +37,7 @@ In this article, you'll learn how to:
 
 ## Prerequisites
 
+
 * The data that you want to label, either in local files or in Azure blob storage.
 * The set of labels that you want to apply.
 * The instructions for labeling.
@@ -47,12 +48,13 @@ In this article, you'll learn how to:
 
 Labeling projects are administered from Azure Machine Learning. You use the **Labeling projects** page to manage your projects and people. A project has one or more teams assigned to it, and a team has one or more people assigned to it.
 
-If your data is already in Azure Blob storage, you should make it available as a datastore before you create the labeling project. For details, see [Create and register datastores](https://docs.microsoft.com/azure/machine-learning/how-to-access-data#create-and-register-datastores).
+If your data is already in Azure Blob storage, you should make it available as a datastore before you create the labeling project. For an example of using a datastore, see [Tutorial: Create your first image classification labeling project](tutorial-labeling.md).
 
 To create a project, select **Add project**. Give the project an appropriate name and select **Labeling task type**.
 
 ![Labeling project creation wizard](./media/how-to-create-labeling-projects/labeling-creation-wizard.png)
 
+
 * Choose **Image Classification Multi-class** for projects when you want to apply only a *single class* from a set of classes to an image.
 * Choose **Image Classification Multi-label** for projects when you want to apply *one or more* labels from a set of classes to an image. For instance, a photo of a dog might be labeled with both *dog* and *daytime*.
 * Choose **Object Identification (Bounding Box)** for projects when you want to assign a class and a bounding box to each object within an image.
@@ -164,9 +166,9 @@ After the labeling project is initialized, some aspects of the  project are immu
 
 ## Manage teams and people
 
-By default, each labeling project that you create gets a new team with you as a member. But teams can also be shared between projects. And projects can have more than one team. To create a team, select **Add team** on the **Teams** page.
+By default, each labeling project that you create gets a new team with you as a member. But teams can also be shared between projects. And projects can have more than one team. To create a team, select **Add team** on the **Teams** page. 
 
-You manage people on the **People** page. Add and remove people by email address. Each labeler has to authenticate through your Microsoft account or Azure Active Directory, if you use it.  
+You manage people on the **Labelers** page. Add and remove people by email address. Each labeler has to authenticate through your Microsoft account or Azure Active Directory, if you use it.  
 
 After you add a person, you can assign that person to one or more teams: Go to the **Teams** page, select the team, and then select **Assign people** or **Remove people**.
 
@@ -213,5 +215,6 @@ The COCO file is created in the default blob store of the Azure Machine Learning
 
 ## Next steps
 
+* [Tutorial: Create your first image classification labeling project](tutorial-labeling.md).
 * Label images for [image classification or object detection](how-to-label-images.md)
 * Learn more about [Azure Machine Learning and Machine Learning Studio (classic)](compare-azure-ml-to-studio-classic.md)

articles/machine-learning/how-to-label-images.md

@@ -12,7 +12,7 @@ ms.date: 11/04/2019
 
 # Tag images in a labeling project
 
-After your project administrator [creates a labeling project](https://docs.microsoft.com/azure/machine-learning/how-to-create-labeling-projects#create-a-labeling-project) in Azure Machine Learning, you can use the labeling tool to rapidly prepare data for a Machine Learning project. This article describes:
+After your project administrator [creates a labeling project](https://docs.microsoft.com/azure/machine-learning/how-to-create-labeling-projects#create-a-labeling-project) in Azure Machine Learning, you can use the labeling tool (public preview) to rapidly prepare data for a Machine Learning project. This article describes:
 
 > [!div class="checklist"]
 > * How to access your labeling projects
@@ -41,7 +41,9 @@ Go to **View detailed instructions**. These instructions are specific to your pr
 
 In all image-labeling tasks, you choose an appropriate tag or tags from a set that's specified by the project administrator. You can select the first nine tags by using the number keys on your keyboard.  
 
-In image-classification tasks, you can choose to view multiple images simultaneously. Use the icons above the image area to select the layout. To select all the displayed images simultaneously, use **Select all**. To select individual images, use the circular selection button in the upper-right corner of the image. You must select at least one image to apply a tag. If you select multiple images, any tag that you select will be applied to all the selected images.
+In image-classification tasks, you can choose to view multiple images simultaneously. Use the icons above the image area to select the layout. 
+
+To select all the displayed images simultaneously, use **Select all**. To select individual images, use the circular selection button in the upper-right corner of the image. You must select at least one image to apply a tag. If you select multiple images, any tag that you select will be applied to all the selected images.
 
 Here we've chosen a two-by-two layout and are about to apply the tag "Mammal" to the images of the bear and orca. The image of the shark was already tagged as "Cartilaginous fish," and the iguana hasn't been tagged yet.
 

articles/machine-learning/media/tutorial-labeling/catsndogs.gif

@@ -18,14 +18,12 @@
   items:
   - name: Studio & Designer
     items:
-    - name: Designer (drag-and-drop)
-      items:
-      - name: 'Designer: 1. Train a regression model'
-        displayName: studio
-        href: tutorial-designer-automobile-price-train-score.md
-      - name: 'Designer: 2. Deploy that model'
-        displayName: studio
-        href: tutorial-designer-automobile-price-deploy.md
+    - name: 'Designer: 1. Train a regression model'
+      displayName: studio
+      href: tutorial-designer-automobile-price-train-score.md
+    - name: 'Designer: 2. Deploy that model'
+      displayName: studio
+      href: tutorial-designer-automobile-price-deploy.md
     - name: Automated ML (UI)
       items:
       - name: Create an automated ML experiment
@@ -34,6 +32,9 @@
       - name: Forecast demand with automated ML (Bike share data)
         displayName: automl, automated, auto ml, portal, ui
         href: tutorial-automated-ml-forecast.md
+    - name: Label image data
+      displayName: tag, tagging, images
+      href: tutorial-labeling.md
   - name: Python SDK
     items:
     - name: Create first ML experiment