blanca's changes

Author: lgayhardt

articles/machine-learning/how-to-create-component-pipeline-python.md

@@ -20,7 +20,7 @@ In this article, you learn how to build an [Azure Machine Learning pipeline](con
 
 The example trains a small [Keras](https://keras.io/) convolutional neural network to classify images in the [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) dataset. The pipeline looks like following.
 
-:::image type="content" source="./media/how-to-create-component-pipeline-python-v2/pipeline-graph.png" alt-text="Screenshot showing pipeline graph of the image classification Keras example." lightbox ="./media/how-to-create-component-pipeline-python-v2/pipeline-graph.png":::
+:::image type="content" source="./media/how-to-create-component-pipeline-python/pipeline-graph.png" alt-text="Screenshot showing pipeline graph of the image classification Keras example." lightbox ="./media/how-to-create-component-pipeline-python/pipeline-graph.png":::
 
 
 In this article, you complete the following tasks:
@@ -53,7 +53,7 @@ If you don't have an Azure subscription, create a free account before you begin.
 
 This article uses the Python SDK for Azure ML to create and control an Azure Machine Learning pipeline. The article assumes that you'll be running the code snippets interactively in either a Python REPL environment or a Jupyter notebook.
 
-This article is based on the `image_classification_keras_minist_convnet.ipynb` notebook found in the `sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet` directory of the [AzureML Examples](https://github.com/azure/azureml-examples) repository.
+This article is based on the [image_classification_keras_minist_convnet.ipynb](https://github.com/Azure/azureml-examples/blob/sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/image_classification_keras_minist_convnet.ipynb) notebook found in the `sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet` directory of the [AzureML Examples](https://github.com/azure/azureml-examples) repository.
 
 ## Import required libraries
 
@@ -103,8 +103,7 @@ If you're following along with the example in the [AzureML Examples repo](https:
 
 By using command_component() function as a decorator, you can easily define the component's interface, metadata and code to execute from a python function. Each decorated Python function will be transformed into a single static specification (YAML) that the pipeline service can process.
 
-
-:::code language="python" source="~/azureml-examples-sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/prep/prep_dsl_component.py":::
+:::code language="python" source="~/azureml-examples-sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/prep/prep_component.py":::
 
 The code above define a component with display name `Prep Data` using `@command_component` decorator:
 
@@ -122,14 +121,13 @@ Following is what a component looks like in the studio UI.
 - A component is a block in a pipeline graph.
 - The `input_data`, `training_data` and `test_data` are ports of the component, which connects to other components for data streaming.
 
-:::image type="content" source="./media/how-to-create-component-pipeline-python-v2/prep-data-component.png" alt-text="Screenshot of the Prep Data component in the UI and code." lightbox ="./media/how-to-create-component-pipeline-python-v2/prep-data-component.png":::
+:::image type="content" source="./media/how-to-create-component-pipeline-python/prep-data-component.png" alt-text="Screenshot of the Prep Data component in the UI and code." lightbox ="./media/how-to-create-component-pipeline-python/prep-data-component.png":::
 
 #### Specify component run-time environment
 
 You'll need to modify the runtime environment in which your component runs.
 
-:::code language="python" source="~/azureml-examples-sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/prep/prep_dsl_component.py" range="5-10":::
-
+:::code language="python" source="~/azureml-examples-sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/prep/prep_component.py" range="5-10":::
 
 The above code creates an object of `Environment` class, which represents the runtime environment in which the component runs.
 
@@ -161,7 +159,7 @@ The `train.py` file contains a normal python function, which performs the traini
 
 After defining the training function successfully, you can use @command_component in Azure Machine Learning SDK v2 to wrap your function as a component, which can be used in AML pipelines.
 
-:::code language="python" source="~/azureml-examples-sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/train/train_dsl_component.py":::
+:::code language="python" source="~/azureml-examples-sdk-preview/sdk/jobs/pipelines/2e_image_classification_keras_minist_convnet/train/train_component.py":::
 
 The code above define a component with display name `Train Image Classification Keras` using `@command_component`:
 
@@ -175,7 +173,6 @@ The train-model component has a slightly more complex configuration than the pre
 
 Now, you've prepared all source files for the `Train Image Classification Keras` component.
 
-
 ### Create the score-model component
 
 In this section, other than the previous components, you'll create a component to score the trained model via Yaml specification and script.
@@ -303,9 +300,9 @@ You can monitor the pipeline run by opening the link or you can block until it c
 
 You can open the `Link to Azure Machine Learning studio`, which is the job detail page of your pipeline. You'll see the pipeline graph like following.
 
-:::image type="content" source="./media/how-to-create-component-pipeline-python-v2/pipeline-ui.png" alt-text="Screenshot of the pipeline job detail page." lightbox ="./media/how-to-create-component-pipeline-python-v2/pipeline-ui.png":::
+:::image type="content" source="./media/how-to-create-component-pipeline-python/pipeline-ui.png" alt-text="Screenshot of the pipeline job detail page." lightbox ="./media/how-to-create-component-pipeline-python/pipeline-ui.png":::
 
-You can check the logs and outputs of each component by right clicking the component, or select the component to open its detail pane. To learn more about how to debug your pipeline in UI, you can refer to [this article](how-to-use-pipeline-ui.md).
+You can check the logs and outputs of each component by right clicking the component, or select the component to open its detail pane. To learn more about how to debug your pipeline in UI, see [How to use studio UI to build and debug Azure ML pipelines](how-to-use-pipeline-ui.md).
 
 ## (Optional) Register components to workspace
 

articles/machine-learning/how-to-create-component-pipelines-ui.md

@@ -38,7 +38,7 @@ In this article, you'll learn how to create and run [machine learning pipelines]
 
 ## Register component in your workspace
 
-To build pipeline using components in UI, you need to register components to your workspace first. You can use CLI or SDK to register components to your workspace, so that you can share and resue the component within the workspace. Registered components support automatic versioning so you can update the component but assure that pipelines that require an older version will continue to work.  
+To build pipeline using components in UI, you need to register components to your workspace first. You can use CLI or SDK to register components to your workspace, so that you can share and reuse the component within the workspace. Registered components support automatic versioning so you can update the component but assure that pipelines that require an older version will continue to work.  
 
 In the example below take using CLI for example. If you want to learn more about how to build a component, see [Create and run pipelines using components with  CLI](how-to-create-component-pipelines-cli.md).
 
@@ -58,16 +58,20 @@ In the example below take using CLI for example. If you want to learn more about
 
 ## Create pipeline using registered component
 
-1. Create a new pipeline in the designer. You can change the pipeline name.
+1. Create a new pipeline in the designer.
+
+        :::image type="content" source="./media/how-to-create-component-pipelines-ui/new-pipeline.png" alt-text="Screenshot showing creating new pipeline in designer homepage." lightbox ="./media/how-to-create-component-pipelines-ui/new-pipeline.png":::
 
 1. Set the default compute target of the pipeline. 
 
-    Next to the pipeline name, select the **Gear icon** ![Screenshot of the gear icon that is in the UI.](./media/tutorial-designer-automobile-price-train-score/gear-icon.png) at the top of the canvas to open the **Settings** pane. Select the default compute target for your pipeline.
+    Select the **Gear icon** ![Screenshot of the gear icon that is in the UI.](./media/tutorial-designer-automobile-price-train-score/gear-icon.png) at the top right of the canvas to open the **Settings** pane. Select the default compute target for your pipeline.
+
+    :::image type="content" source="./media/how-to-create-component-pipelines-ui/set-default-compute.png" alt-text="Screenshot showing setting default compute for the pipeline." lightbox ="./media/how-to-create-component-pipelines-ui/set-default-compute.png":::
 
     > [!Important]
     > Attached compute is not supported, use [compute instances or clusters](concept-compute-target.md#azure-machine-learning-compute-managed) instead.
 
-1. In asset library, you can see the components registered from previous section.
+1. In asset library, you can see **Data assets** and **Components** tabs. Switch to **Components** tab, you can see the components registered from previous section.
 
     :::image type="content" source="./media/how-to-create-component-pipelines-ui/asset-library.png" alt-text="Screenshot showing registered component in asset library." lightbox ="./media/how-to-create-component-pipelines-ui/asset-library.png":::
 

articles/machine-learning/how-to-use-pipeline-ui.md

@@ -23,7 +23,7 @@ This article will introduce how to use the studio UI to build and debug machine
 
 ### Drag and drop components to build pipeline
 
-In the designer homepage, you can select "New" to open a blank pipeline draft.
+In the designer homepage, you can select **New pipeline** to open a blank pipeline draft.
 
 In the asset library left of the canvas, there are **Data assets** and **Components** tabs, which contain components and data registered to the workspace. For what is component and how to create custom component, you can refer to the [component concept article](concept-component.md).
 
@@ -73,7 +73,7 @@ You can filter failed or completed nodes, and filter by only components or datas
 
 :::image type="content" source="./media/how-to-use-pipeline-ui/quick-filter.png" alt-text="Screenshot showing the quick filter by in outline > search." lightbox= "./media/how-to-use-pipeline-ui/quick-filter.png":::
 
-You can also sort the filter nodes.
+You can also sort the filtered nodes.
 
 :::image type="content" source="./media/how-to-use-pipeline-ui/sort.png" alt-text="Screenshot of sorting search result in outline > search." lightbox= "./media/how-to-use-pipeline-ui/sort.png":::