Merge branch 'main' of https://github.com/windows-toolkit/Labs-IntelligentAPIs into main

.github/workflows/build.yml

@@ -24,6 +24,11 @@ jobs:
 
     # Steps represent a sequence of tasks that will be executed as part of the job
     steps:
+      - name: Install .NET Core SDK
+        uses: actions/setup-dotnet@v1
+        with:
+          dotnet-version: '5.0.302'
+
       # Checks-out your repository under $GITHUB_WORKSPACE, so your job can access it
       - uses: actions/checkout@v2
       # Runs a set of commands using the runners shell
@@ -38,11 +43,11 @@ jobs:
 
       - name: pack ImageClassifier
         working-directory: ./ImageClassifier
-        run: msbuild -t:pack
+        run: msbuild -t:pack /p:Configuration=Release
                      
       - name: pack ObjectDetector
         working-directory: ./ObjectDetector
-        run: msbuild -t:pack
+        run: msbuild -t:pack /p:Configuration=Release
         
       - name: Add source
         run: dotnet nuget add source "https://pkgs.dev.azure.com/dotnet/CommunityToolkit/_packaging/CommunityToolkit-Labs/nuget/v3/index.json" --name LabsFeed --username dummy --password ${{ secrets.DEVOPS_PACKAGE_PUSH_TOKEN }}
@@ -51,4 +56,3 @@ jobs:
         run: dotnet nuget push "**/*.nupkg" --api-key dummy --source LabsFeed --skip-duplicate
 
 
-

README.md

@@ -5,7 +5,9 @@
 ## What are Intelligent APIs?
 
 Intelligent APIs aim to make machine learning tasks easier for developers to leverage in their applications without needing ML expertise or creating a new model.
-By just importing a nuget package and calling a function, we want developers to be able to build intelligent app experiences without needing to deal with the complexities of inferencing machine learning models on Windows.
+By just importing a nuget package and calling a function, we want developers to be able to build intelligent app experiences without needing to deal with the complexities of inferencing machine learning models on Windows. 
+
+Each of these APIs employs WinML (Windows Machine Learning) to use the models on Windows. WinML helps abstract a lot of the model-specific code away and performs hardware optimizations to improve performance significantly on Windows. Learn more about WinML [here](https://docs.microsoft.com/en-us/windows/ai/windows-ml/). 
 
 ## Pre-requisites