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docs/machine-learning/resources/azure-training-concepts-model-builder.md

@@ -31,10 +31,10 @@ To create an Azure Machine Learning workspace, the following are required:
 
 An Azure Machine Learning compute is a cloud-based Linux VM used for training.
 
-To create an Azure Machine Learning compute, the following are required:
+To create an Azure Machine Learning compute, the following values are required:
 
 - Name: A name for your compute between 2-16 characters. Names may only contain alphanumeric characters and hyphens.
-- Compute size
+- Compute size.
 
     Model Builder can use one of the following GPU-optimized compute types:
 
@@ -44,10 +44,10 @@ To create an Azure Machine Learning compute, the following are required:
     | Standard_NC24   | 24 | 224 | 1440 | 4 | 48 | 64 | 4 |
 
     Visit the [NC-series Linux VM documentation](/azure/virtual-machines/nc-series?bc=%252fazure%252fvirtual-machines%252flinux%252fbreadcrumb%252ftoc.json&toc=%252fazure%252fvirtual-machines%252flinux%252ftoc.json) for more details on GPU optimized compute types.
-- Compute priority
+- Compute priority.
 
-  - Low-priority: Suited for tasks with shorter execution times. Can be impacted by interruptions and lack of availability. Usually costs less because it takes advantage of surplus capacity in Azure.
-  - Dedicated: Suited for tasks of any duration, but especially long-running jobs. Not impacted by interruptions or lack of availability. Usually costs more because it reserves a dedicated set of compute resources in Azure for your tasks.
+  - Low-priority: Suited for tasks with shorter execution times. Tasks can be impacted by interruptions and lack of availability. This option usually costs less because it takes advantage of surplus capacity in Azure.
+  - Dedicated: Suited for tasks of any duration, but especially long-running jobs. Tasks aren't impacted by interruptions or lack of availability. This option usually costs more because it reserves a dedicated set of compute resources in Azure for your tasks.
 
 ## Training
 

docs/machine-learning/snippets/mldotnet-api/csharp/Program.cs

@@ -1,15 +1,15 @@
-using Microsoft.ML;
+using Microsoft.ML;
 using Microsoft.ML.Data;
 
 class Program
 {
-    public class HouseData
+    public record HouseData
     {
         public float Size { get; set; }
         public float Price { get; set; }
     }
 
-    public class Prediction
+    public record Prediction
     {
         [ColumnName("Score")]
         public float Price { get; set; }
@@ -21,23 +21,23 @@ static void Main(string[] args)
 
         // 1. Import or create training data.
         HouseData[] houseData = [
-                new HouseData() { Size = 1.1F, Price = 1.2F },
-                new HouseData() { Size = 1.9F, Price = 2.3F },
-                new HouseData() { Size = 2.8F, Price = 3.0F },
-                new HouseData() { Size = 3.4F, Price = 3.7F }
+                new() { Size = 1.1F, Price = 1.2F },
+                new() { Size = 1.9F, Price = 2.3F },
+                new() { Size = 2.8F, Price = 3.0F },
+                new() { Size = 3.4F, Price = 3.7F }
                 ];
         IDataView trainingData = mlContext.Data.LoadFromEnumerable(houseData);
 
         // 2. Specify data preparation and model training pipeline.
-        var pipeline = mlContext.Transforms.Concatenate("Features", ["Size"])
+        EstimatorChain<RegressionPredictionTransformer<Microsoft.ML.Trainers.LinearRegressionModelParameters>> pipeline = mlContext.Transforms.Concatenate("Features", ["Size"])
             .Append(mlContext.Regression.Trainers.Sdca(labelColumnName: "Price", maximumNumberOfIterations: 100));
 
         // 3. Train model.
-        var model = pipeline.Fit(trainingData);
+        TransformerChain<RegressionPredictionTransformer<Microsoft.ML.Trainers.LinearRegressionModelParameters>> model = pipeline.Fit(trainingData);
 
         // 4. Make a prediction.
-        var size = new HouseData() { Size = 2.5F };
-        var price = mlContext.Model.CreatePredictionEngine<HouseData, Prediction>(model).Predict(size);
+        HouseData size = new() { Size = 2.5F };
+        Prediction price = mlContext.Model.CreatePredictionEngine<HouseData, Prediction>(model).Predict(size);
 
         Console.WriteLine($"Predicted price for size: {size.Size * 1000} sq ft = {price.Price * 100:C}k");