Revert "Migration/v1.2.0 (#568)" (#569)

This reverts commit 81ec280.
dotnet · Jul 19, 2019 · ced29a6 · ced29a6
1 parent 81ec280
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diff --git a/samples/Directory.Build.props b/samples/Directory.Build.props
@@ -1,8 +1,8 @@
 <Project>
 
   <PropertyGroup>
-    <MicrosoftMLVersion>1.2.0</MicrosoftMLVersion>
-    <MicrosoftMLPreviewVersion>0.14.0</MicrosoftMLPreviewVersion>
+    <MicrosoftMLVersion>1.1.0</MicrosoftMLVersion>
+    <MicrosoftMLPreviewVersion>0.13.0</MicrosoftMLPreviewVersion>
   </PropertyGroup>
 
 </Project>
diff --git a/samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md b/samples/csharp/end-to-end-apps/AnomalyDetection-Sales/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0         | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Product Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
+| v1.1.0         | Dynamic API | Up-to-date | WinForms app | .csv files | Spike and Change Point Detection of Product Sales | Anomaly Detection | IID Spike Detection and IID Change point Detection |
 
 ![Alt Text](./SpikeDetectionE2EApp/SpikeDetection.WinForms/images/productsales.gif)
 

diff --git a/...es/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md b/...es/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model  | DeepLearning model |
+| v1.1.0           | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow model  | DeepLearning model |
 
 
 ## Problem

diff --git a/...ssification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj b/...ssification_TensorFlow/TensorFlowImageClassification/TensorFlowImageClassification.csproj
@@ -9,10 +9,10 @@
   <ItemGroup>
     <PackageReference Include="Microsoft.AspNetCore.App" />
     <PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
-    <PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
-    <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
-    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
-    <PackageReference Include="Microsoft.ML.TensorFlow" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
+    <PackageReference Include="Microsoft.ML" Version="1.1.0" />
+    <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="1.1.0" />
+    <PackageReference Include="Microsoft.ML.TensorFlow" Version="0.13.0" />
     <PackageReference Include="Microsoft.VisualStudio.Web.CodeGeneration.Design" Version="2.2.3" />
   </ItemGroup>
 

diff --git a/samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md b/samples/csharp/end-to-end-apps/DeepLearning_ObjectDetection_Onnx/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning  | Tiny Yolo2 ONNX model |
+| v1.1.0           | Dynamic API | Up-to-date | End-End app | image files | Object Detection | Deep Learning  | Tiny Yolo2 ONNX model |
 
 ## Problem 
 Object detection is one of the classical problems in computer vision: Recognize what objects are inside a given image and also where they are in the image. For these cases, you can either use pre-trained models or train your own model to classify images specific to your custom domain. 

diff --git a/samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md b/samples/csharp/end-to-end-apps/MulticlassClassification-GitHubLabeler/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data sources | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
+| v1.1.0           | Dynamic API | Up-to-date | Console app | .csv file and GitHub issues | Issues classification | Multi-class classification | SDCA multi-class classifier, AveragedPerceptronTrainer |
 
 
 This is a simple prototype application to demonstrate how to use [ML.NET](https://www.nuget.org/packages/Microsoft.ML/) APIs. The main focus is on creating, training, and using ML (Machine Learning) model that is implemented in Predictor.cs class.

diff --git a/...Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj b/...Recommendation-MovieRecommender/MovieRecommender/movierecommender/MovieRecommender.csproj
@@ -10,7 +10,7 @@
     <PackageReference Include="Microsoft.AspNetCore.App" />
     <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
     <PackageReference Include="Microsoft.AspNetCore.Razor.Design" Version="2.2.0" PrivateAssets="All" />
-    <PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
+    <PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
   </ItemGroup>
   <ItemGroup>
     <Content Update="wwwroot\images\smileybob.png">

diff --git a/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md b/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data sources | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-|v1.2.0     | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines |
+|v1.1.0     | Dynamic API | up-to-date | End-End app | .csv | Movie Recommendation | Recommendation | Field Aware Factorization Machines |
 
 ![Alt Text](https://github.com/dotnet/machinelearning-samples/blob/master/samples/csharp/end-to-end-apps/Recommendation-MovieRecommender/MovieRecommender/movierecommender/wwwroot/images/movierecommender.gif)
 

diff --git a/samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md b/samples/csharp/end-to-end-apps/Regression-SalesForecast/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression |
+| v1.1.0           | Dynamic API | Up-to-date | ASP.NET Core web app and Console app | SQL Server and .csv files | Sales forecast | Regression | FastTreeTweedie Regression |
 
 
 eShopDashboardML is a web app with Sales Forecast predictions (per product and per country) using [Microsoft Machine Learning .NET (ML.NET)](https://github.com/dotnet/machinelearning).

diff --git a/.../csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj b/.../csharp/end-to-end-apps/Regression-SalesForecast/src/eShopDashboard/eShopDashboard.csproj
@@ -20,7 +20,7 @@
     <PackageReference Include="Serilog.Sinks.Seq" Version="4.0.0" />
     <PackageReference Include="Swashbuckle.AspNetCore" Version="4.0.1" />
     <PackageReference Include="TinyCsvParser" Version="2.0.0" />
-    <PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
+    <PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
   </ItemGroup>
 
   <ItemGroup>

diff --git a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI-IntegrationPkg/README.md
@@ -5,7 +5,7 @@
 
 | ML.NET version | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
+| v1.1.0           | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
 
 
 **This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.

diff --git a/...nd-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj b/...nd-apps/ScalableMLModelOnWebAPI-IntegrationPkg/src/Scalable.WebAPI/Scalable.WebAPI.csproj
@@ -6,7 +6,7 @@
   </PropertyGroup>
 
   <ItemGroup>
-    <PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
+    <PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
     <PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
     <PackageReference Include="Microsoft.AspNetCore.App" />
   </ItemGroup>

diff --git a/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md b/samples/csharp/end-to-end-apps/ScalableMLModelOnWebAPI/README.md
@@ -5,7 +5,7 @@
 
 | ML.NET version | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
+| v1.0.0           | Up-to-date | ASP.NET Core 2.2 WebAPI | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
 
 
 **This posts explains how to optimize your code when running an ML.NET model on an ASP.NET Core WebAPI service.** The code would be very similar when running it on an ASP.NET Core MVC or Razor web app, too.

diff --git a/...AnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj b/...AnalysisBlazorWebApp/BlazorSentimentAnalysis.Server/BlazorSentimentAnalysis.Server.csproj
@@ -12,8 +12,8 @@
   <ItemGroup>
     <PackageReference Include="Microsoft.AspNetCore.Blazor.Server" Version="3.0.0-preview6.19307.2" />
     <PackageReference Include="Microsoft.AspNetCore.Mvc.NewtonsoftJson" Version="3.0.0-preview6.19307.2" />
-    <PackageReference Include="Microsoft.Extensions.ML" Version="$(MicrosoftMLPreviewVersion)" />
-    <PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.Extensions.ML" Version="0.12.0" />
+    <PackageReference Include="Microsoft.ML.FastTree" Version="1.1.0" />
   </ItemGroup>
 
   <ItemGroup>

diff --git a/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md b/samples/csharp/end-to-end-apps/ScalableSentimentAnalysisBlazorWebApp/README.md
@@ -5,7 +5,7 @@
 
 | ML.NET version | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
+| v1.1.0           | Up-to-date | Blazor / ASP.NET Core 3.0 Preview 6 | Single data sample | Sentiment Analysis | Binary   classification | Linear Classification |
 
 # Goal
 

diff --git a/samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md b/samples/csharp/getting-started/AnomalyDetection_PowerMeterReadings/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
+| v1.1.0           | Dynamic API | Up-to-date | Console app | .csv files | Power Meter Anomaly Detection | Time Series- Anomaly Detection | SsaSpikeDetection |
 
 In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect anomalies in time series data.
 

diff --git a/samples/csharp/getting-started/AnomalyDetection_Sales/README.md b/samples/csharp/getting-started/AnomalyDetection_Sales/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0         | Dynamic API | Up-to-date | Console app | .csv files | Product Sales Spike Detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
+| v1.1.0         | Dynamic API | Up-to-date | Console app | .csv files | Product Sales Spike Detection| Time Series - Anomaly Detection | IID Spike Detection and IID Change point Detection |
 
 In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to detect **spikes** and **change points** in Product sales. In the world of machine learning, this type of task is called TimeSeries Anomaly Detection.
 

diff --git a/.../csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md b/.../csharp/getting-started/BinaryClassification_CreditCardFraudDetection/Readme.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
+| v1.1.0           | Dynamic API | Up-to-date | Two console apps | .csv file | Fraud Detection | Two-class classification | FastTree Binary Classification |
 
 In this introductory sample, you'll see how to use ML.NET to predict a credit card fraud. In the world of machine learning, this type of prediction is known as binary classification.
 

diff --git a/...les/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md b/...les/csharp/getting-started/BinaryClassification_HeartDiseaseDetection/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
+| v1.1.0           | Dynamic API | Up-to-date | Console app | .txt files | Heart disease classification | Binary classification | FastTree |
 
 In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict type of heart disease. In the world of machine learning, this type of prediction is known as **binary classification**.
 

diff --git a/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md b/samples/csharp/getting-started/BinaryClassification_SentimentAnalysis/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0          | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class  classification | Linear Classification |
+| v1.1.0          | Dynamic API | up-to-date | Console app | .tsv files | Sentiment Analysis | Two-class  classification | Linear Classification |
 
 In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict a sentiment (positive or negative) for customer reviews. In the world of machine learning, this type of prediction is known as **binary classification**.
 

diff --git a/samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md b/samples/csharp/getting-started/BinaryClassification_SpamDetection/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) |
+| v1.1.0           | Dynamic API | Might need to update project structure to match template | Console app | .tsv files | Spam detection | Two-class classification | Averaged Perceptron (linear learner) |
 
 In this sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to predict whether a text message is spam. In the world of machine learning, this type of prediction is known as **binary classification**.
 

diff --git a/samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md b/samples/csharp/getting-started/Clustering_CustomerSegmentation/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0          | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
+| v1.1.0          | Dynamic API | Up-to-date | Console app | .csv files | Customer segmentation | Clustering | K-means++ |
 
 ## Problem
 

diff --git a/samples/csharp/getting-started/Clustering_Iris/READMe.md b/samples/csharp/getting-started/Clustering_Iris/READMe.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0         | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
+| v1.1.0         | Dynamic API | Up-to-date | Console app | .txt file | Clustering Iris flowers | Clustering | K-means++ |
 
 In this introductory sample, you'll see how to use [ML.NET](https://www.microsoft.com/net/learn/apps/machine-learning-and-ai/ml-dotnet) to divide iris flowers into different groups that correspond to different types of iris. In the world of machine learning, this task is known as **clustering**.
 

diff --git a/...csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj b/...csharp/getting-started/DatabaseIntegration/DatabaseIntegration/DatabaseIntegration.csproj
@@ -8,9 +8,9 @@
   <ItemGroup>
     <PackageReference Include="Microsoft.EntityFrameworkCore.Design" Version="3.0.0-preview.19074.3" />
     <PackageReference Include="Microsoft.EntityFrameworkCore.Sqlite" Version="3.0.0-preview.19074.3" />
-    <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
-    <PackageReference Include="Microsoft.ML.LightGBM" Version="$(MicrosoftMLVersion)" />
-    <PackageReference Include="Microsoft.ML.FastTree" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.ML" Version="1.0.0-preview" />
+    <PackageReference Include="Microsoft.ML.LightGBM" Version="1.0.0-preview" />
+    <PackageReference Include="Microsoft.ML.FastTree" Version="1.0.0-preview" />
   </ItemGroup>
   <ItemGroup>
     <Folder Include="Common\" />

diff --git a/...es/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md b/...es/csharp/getting-started/DeepLearning_ImageClassification_TensorFlow/README.md
@@ -2,7 +2,7 @@
 
 | ML.NET version | API type          | Status                        | App Type    | Data type | Scenario            | ML Task                   | Algorithms                  |
 |----------------|-------------------|-------------------------------|-------------|-----------|---------------------|---------------------------|-----------------------------|
-| v1.2.0           | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h  | DeepLearning model |
+| v1.1.0           | Dynamic API | up-to-date | Console app | Images and text labels | Images classification | TensorFlow Inception5h  | DeepLearning model |
 
 
 ## Problem

diff --git a/...tarted/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj b/...tarted/DeepLearning_ObjectDetection_Onnx/ObjectDetectionConsoleApp/ObjectDetection.csproj
@@ -30,7 +30,7 @@
   <ItemGroup>
     <PackageReference Include="Microsoft.ML" Version="$(MicrosoftMLVersion)" />
     <PackageReference Include="Microsoft.ML.ImageAnalytics" Version="$(MicrosoftMLVersion)" />
-    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLVersion)" />
+    <PackageReference Include="Microsoft.ML.OnnxTransformer" Version="$(MicrosoftMLPreviewVersion)" />
   </ItemGroup>
 
   <ItemGroup>