Added link to PlaneIdentifier sample

README.md

@@ -29,6 +29,8 @@ These generic examples show how to use various models and input feeds with Windo
 - **[MNIST\UWP\cppcx](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/MNIST/UWP)**: a UWP C++/CX app that uses the MNIST model to detect numberic characters.
 - **[CustomTensorization](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/CustomTensorization)**: a Windows Console Application (C++/WinRT) that shows how to do custom tensorization.
 - **[Emoji8](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Samples/Emoji8)**: a UWP C# app that uses the FER Emotion model to evaluate how well the user's facial expressions match randomly selected emojis.
+- **[PlaneIdentifier](https://github.com/Microsoft/Windows-AppConsult-Samples-UWP/tree/master/PlaneIdentifier)**: a UWP app and a WPF app packaged with the Desktop Bridge, sharing the same model trained using [Azure Custom Vision service](https://customvision.ai/). For step-by-step instructions for this sample, please see the blog post [Upgrade your WinML application to the latest bits](https://blogs.msdn.microsoft.com/appconsult/2018/11/06/upgrade-your-winml-application-to-the-latest-bits/).
+
 ## Developer Tools
 - **[WinMLRunner](https://github.com/Microsoft/Windows-Machine-Learning/tree/master/Tools/WinMLRunner)**: a command-line tool that can run .onnx or .pb models where the input and output variables are tensors or images. It is a very handy tool to quickly validate an ONNX model. It will attempt to load, bind, and evaluate a model and print out helpful messages. It also captures performance measurements.