Merge pull request #164519 from avkewalr-msft/patch-50

Updated as per feedback fixes

Author: v-ccolin

articles/azure-video-analyzer/video-analyzer-docs/analyze-live-video-custom-vision.md

@@ -108,24 +108,9 @@ After you're finished, you can export the model to a Docker container by using t
    2. `docker image ls`
 
       This command checks if the new image is in your local registry.
-   3. `docker run -p 127.0.0.1:80:80 -d cvtruck`
-
-      This command should publish the Docker's exposed port (80) onto your local machine's port (80).
-   4. `docker container ls`
-
-      This command checks the port mappings and if the Docker container is running successfully on your machine. The output should be something like:
-      
-      ```
-      CONTAINER ID        IMAGE               COMMAND                  CREATED             STATUS              PORTS                      NAMES
-      8b7505398367        cvtruck             "/bin/sh -c 'python …"   13 hours ago        Up 25 seconds       127.0.0.1:80->80/tcp   practical_cohen
-      ```
-   5. `curl -X POST http://127.0.0.1:80/score -F imageData=@<path to any image file that has the toy delivery truck in it>`
-
-      This command tests the container on the local machine. If the image has the same delivery truck as we trained the model on, the output should be something like the following example. It suggests the delivery truck was detected with 90.12% probability.
-
-      ```
-      {"created":"2020-03-20T07:10:47.827673","id":"","iteration":"","predictions":[{"boundingBox":{"height":0.66167289,"left":-0.03923762,"top":0.12781593,"width":0.70003178},"probability":0.90128148,"tagId":0,"tagName":"delivery truck"},{"boundingBox":{"height":0.63733053,"left":0.25220079,"top":0.0876643,"width":0.53331227},"probability":0.59745145,"tagId":0,"tagName":"delivery truck"}],"project":""}
-      ```
+   
+## Set up your development environment
+[!INCLUDE [setup development environment](./includes/set-up-dev-environment/csharp/csharp-set-up-dev-env.md)]
 
 ## Examine the sample files
 
@@ -184,15 +169,11 @@ After you're finished, you can export the model to a Docker container by using t
     - A module named `rtspsim`, which simulates an RTSP server that acts as the source of a live video feed.
     - A module named `cv`, which as the name suggests is the Custom Vision toy truck detection model that applies Custom Vision to the images and returns multiple tag types. (Our model was trained on only one tag, delivery truck.)
 
-## Prepare for monitoring events
-
-Right-click the ava-sample-device, and select **Start Monitoring Built-in Event Endpoint**. You need this step to monitor the IoT Hub events in the **OUTPUT** window of Visual Studio Code.
 
-![Screenshot that shows Start Monitoring Built-in Event Endpoint.](./media/custom-vision/start-monitoring.png)
 
 ## Run the sample program
 
-If you open the topology for this tutorial in a browser, you'll see that the value of `inferencingUrl` has been set to `http://cv/image`. This setting means the inference server will return results after detecting toy trucks, if any, in the live video.
+If you open the topology for this tutorial in a browser, you'll see that the value of `inferencingUrl` has been set to `http://cv/score`. This setting means the inference server will return results after detecting toy trucks, if any, in the live video.
 
 1. In Visual Studio Code, open the **Extensions** tab (or select **Ctrl+Shift+X**) and search for Azure IoT Hub.
 2. Right-click and select **Extension Settings**.
@@ -219,7 +200,7 @@ If you open the topology for this tutorial in a browser, you'll see that the val
             "parameters": [
               { 
                 "name": "inferencingUrl",
-                "value": "http://cv/image"
+                "value": "http://cv/score"
               },
               {
                 "name": "rtspUrl",