Merge pull request #103595 from sdgilley/sdg-jedi-pipeline

new article needed for JEDI

Author: TedA-M

articles/machine-learning/how-to-trigger-published-pipeline.md

@@ -0,0 +1,90 @@
+---
+title: Trigger the run of an ML pipeline from a Logic App
+titleSuffix: Azure Machine Learning
+description: Learn how to trigger the run of an ML pipeline by using Azure Logic Apps.
+services: machine-learning
+author: sanpil
+ms.author: sanpil
+ms.service: machine-learning
+ms.subservice: core
+ms.workload: data-services
+ms.topic: conceptual
+ms.date: 02/07/2020
+
+---
+# Trigger a run of a Machine Learning pipeline from a Logic App
+
+Trigger the run of your Azure Machine Learning Pipeline when new data appears. For example, you may want to trigger the pipeline to train a new model when new data appears in the blob storage account. Set up the trigger with [Azure Logic Apps](../logic-apps/logic-apps-overview.md).
+
+## Prerequisites
+
+* An Azure Machine Learning workspace. For more information, see [Create an Azure Machine Learning workspace](how-to-manage-workspace.md).
+
+* The REST endpoint for a published Machine Learning pipeline. [Create and publish your pipeline](how-to-create-your-first-pipeline.md). Then find the REST endpoint of your PublishedPipeline by using the pipeline ID:
+    
+     ```
+    # You can find the pipeline ID in Azure Machine Learning studio
+    
+    published_pipeline = PublishedPipeline.get(ws, id="<pipeline-id-here>")
+    published_pipeline.endpoint 
+    ```
+* [Azure blob storage](../storage/blobs/storage-blobs-overview.md) to store your data.
+* [A datastore](how-to-access-data.md) in your workspace that contains the details of your blob storage account.
+
+## Create a Logic App
+
+Now create an [Azure Logic App](../logic-apps/logic-apps-overview.md) instance. If you wish, [use an integration service environment (ISE)](../logic-apps/connect-virtual-network-vnet-isolated-environment.md) and [set up a customer-managed key](../logic-apps/customer-managed-keys-integration-service-environment.md) for use by your Logic App.
+
+Once your Logic App has been provisioned, use these steps to configure a trigger for your pipeline:
+
+1. [Create a system-assigned managed identity](../logic-apps/create-managed-service-identity.md) to give the app access to your Azure Machine Learning Workspace.
+
+1. Navigate to the Logic App Designer view and select the Blank Logic App template. 
+    > [!div class="mx-imgBorder"]
+    > ![Blank template](media/how-to-trigger-published-pipeline/blank-template.png)
+
+1. In the Designer, search for **blob**. Select the **When a blob is added or modified (properties only)** trigger and add this trigger to your Logic App.
+    > [!div class="mx-imgBorder"]
+    > ![Add trigger](media/how-to-trigger-published-pipeline/add-trigger.png)
+
+1. Fill in the connection info for the Blob storage account you wish to monitor for blob additions or modifications. Select the Container to monitor. 
+ 
+    Choose the **Interval** and **Frequency** to poll for updates that work for you.  
+
+    > [!NOTE]
+    > This trigger will monitor the selected Container but will not monitor subfolders.
+
+1. Add an HTTP action that will run when a new or modified blob is detected. Select **+ New Step**, then search for and select the HTTP action.
+
+  > [!div class="mx-imgBorder"]
+  > ![Search for HTTP action](media/how-to-trigger-published-pipeline/search-http.png)
+
+  Use the following settings to configure your action:
+
+  | Setting | Value | 
+  |---|---|
+  | HTTP action | POST |
+  | URI |the endpoint to the published pipeline that you found as a [Prerequisite](#prerequisites) |
+  | Authentication mode | Managed Identity |
+
+1. Set up your schedule to set the value of any [DataPath PipelineParameters](https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/intro-to-pipelines/aml-pipelines-showcasing-datapath-and-pipelineparameter.ipynb) you may have:
+
+    ```json
+    "DataPathAssignments": { 
+         "input_datapath": { 
+                            "DataStoreName": "<datastore-name>", 
+                            "RelativePath": "@triggerBody()?['Name']" 
+    } 
+    }, 
+    "ExperimentName": "MyRestPipeline", 
+    "ParameterAssignments": { 
+    "input_string": "sample_string3" 
+    },
+    ```
+
+    Use the `DataStoreName` you added to your workspace as a [Prerequisite](#prerequisites).
+     
+    > [!div class="mx-imgBorder"]
+    > ![HTTP settings](media/how-to-trigger-published-pipeline/http-settings.png)
+
+1. Select **Save** and your schedule is now ready.

articles/machine-learning/media/how-to-trigger-published-pipeline/add-trigger.png

@@ -352,6 +352,8 @@
       href: how-to-create-your-first-pipeline.md
     - name: Schedule a pipeline (Python)
       href: how-to-schedule-pipelines.md
+    - name: Trigger a pipeline
+      href: how-to-trigger-published-pipeline.md
     - name: Debug & troubleshoot pipelines
       href: how-to-debug-pipelines.md
     - name: Debug pipelines in Application Insights