ML.NET sample error

open System
open System.ComponentModel.DataAnnotations.Schema
open Microsoft.ML.Data
open Microsoft.ML
open Microsoft.Data.DataView

type IrisData() =
  [<LoadColumn(0);DefaultValue>]
  val mutable public SepalLength: float32

  [<LoadColumn(1);DefaultValue>]
  val mutable public SepalWidth: float32

  [<LoadColumn(2);DefaultValue>]
  val mutable public PetalLength: float32

  [<LoadColumn(3);DefaultValue>]
  val mutable public PetalWidth: float32

  [<LoadColumn(4);DefaultValue>]
  val mutable public Label: string

type IrisPrediction() =
  [<ColumnName("PredictedLabel");DefaultValue>]
  val mutable public PredictedLabel: string


[<EntryPoint>]
let main argv =
    let dataPath = "./iris-data.txt"

    // STEP 2: Create a ML.NET environment
    let mlContext = new MLContext()

    // If working in Visual Studio, make sure to 'Copy to Output Directory'
    // property of iris-data.txt is set to 'Copy always'
    let trainingDataView: IDataView = mlContext.Data.ReadFromTextFile<IrisData>(path = dataPath, hasHeader = false, separatorChar = ',')

    // STEP 3: Transform your data and add a learner
    // Assign numeric values to text in the "Label" column, because only
    // numbers can be processed during model training.
    // Add a learning algorithm to the pipeline. e.g. (What type of iris is this?)
    // Convert the Label back into original text (after converting to number in step 3)
    let transforms = mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth")
    let pipeline = mlContext.Transforms.Conversion.MapValueToKey("Label")
                    // ERROR on this line: Type constraint mismatch. Type type 'Transforms.Conversions.ValueToKeyMappingEstimator' is not compatible with type 'Core.Data.IEstimator<Core.Data.ITransformer>'
                    .Append(mlContext.Transforms.Concatenate("Features", "SepalLength", "SepalWidth", "PetalLength", "PetalWidth"))
                    .AppendCacheCheckpoint(mlContext)
                    .Append(mlContext.MulticlassClassification.Trainers.StochasticDualCoordinateAscent(labelColumn = "Label", featureColumn = "Features"))
                    .Append(mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel"))
    
    // STEP 4
    let model = pipeline.Fit(trainingDataView)

    // STEP 5: Use your model to make a prediction
    // You can change these numbers to test different predictions
    let sampleData  = new IrisData(SepalLength = 3.3f, SepalWidth = 1.6f, PetalLength = 0.2f, PetalWidth = 5.1f)
    let prediction = model.CreatePredictionEngine<IrisData, IrisPrediction>(mlContext).Predict(sampleData)

    Console.WriteLine("Predicted flower type is: " + prediction.PredictedLabel)
    Console.WriteLine("Press any key to exit...")
    Console.ReadLine()

    0 // return an integer exit code