Added testing instructions

Author: AbeOmor

articles/machine-learning/how-to-setup-mlops-github-azureml.md

@@ -229,7 +229,7 @@ This training pipeline contains the following steps:
    - Input: Trained model and the deploy flag.
    - Output: Registered model in Azure Machine Learning.
 
-## Deploying the Model Training Pipeline to the Test Environment
+## Deploying the Model Training Pipeline
 
 Next, you will deploy the model training pipeline to your new Azure Machine Learning workspace. This pipeline will create a compute cluster instance, register a training environment defining the necessary Docker image and python packages, register a training dataset, then start the training pipeline described in the last section. When the job is complete, the trained model will be registered in the Azure ML workspace and be available for deployment.
 
@@ -252,7 +252,7 @@ With the trained model registered in the Azure Machine learning workspace, you a
 
 ### Deploying the Trained Model
 
-This scenario includes prebuilt workflows for two approaches to deploying a trained model, batch scoring or a deploying a model to an endpoint for real-time scoring. You may run either or both of these workflowsto test the performance of the model in your Azure ML workspace.
+This scenario includes prebuilt workflows for two approaches to deploying a trained model, batch scoring or a deploying a model to an endpoint for real-time scoring. You may run either or both of these workflows to test the performance of the model in your Azure ML workspace.
 
 1. In your GitHub project repository (ex: taxi-fare-regression), select **Actions**  
  
@@ -268,6 +268,10 @@ This scenario includes prebuilt workflows for two approaches to deploying a trai
 
    ![AzureML taxi Online endpoint](./media/how-to-setup-mlops-azureml/aml-taxi-oep.png)
 
+1. To test this deployment, go to the **Endpoints** tab in your AzureML workspace, select the endpoint and click the **Test** Tab. You can use the sample input data located in the cloned repo at `/data/taxi-request.json` to test the endpoint.
+   
+    ![AzureML taxi Online endpoint test](./media/how-to-setup-mlops-azureml/azureml-online-endpoint-test.png)
+
 ### Batch Endpoint
       
 1. Select the **deploy-batch-endpoint-pipeline** from the workflows and click **Run workflow** to execute the batch endpoint deployment pipeline workflow. The steps in this pipeline will create a new AmlCompute cluster on which to execute batch scoring, create the batch endpoint in your Azure Machine Learning workspace, then create a deployment of your model to this endpoint.