ML.NET and Azure ML Interview Questions: Machine Learning in .NET

January 25, 2025 • ML.NET • Azure ML • Machine Learning • Interview • .NET

ML.NET and Azure Machine Learning models

ML.NET brings machine learning capabilities directly into the .NET ecosystem, enabling developers to build, train, and deploy ML models using C# and F#. Combined with Azure ML, it provides a powerful platform for .NET developers to implement machine learning solutions. This guide covers essential ML.NET and Azure ML interview questions for .NET developers entering the AI/ML space.

ML.NET Fundamentals

What is ML.NET and When to Use It?

ML.NET is an open-source, cross-platform machine learning framework for .NET developers. It allows you to create custom ML models using C# or F# without leaving the .NET ecosystem.

Use ML.NET when:

You want to keep everything in .NET (no Python dependencies)
You need to integrate ML directly into existing .NET applications
You're working with structured data (tabular data, text classification)
You want to train models locally without cloud services
You need models that can run on edge devices

ML.NET Pipeline Architecture

Understanding the ML.NET pipeline is essential:

using Microsoft.ML;
using Microsoft.ML.Data;

// 1. Create MLContext
var mlContext = new MLContext();

// 2. Load data
var data = mlContext.Data.LoadFromTextFile<SentimentData>(
    path: "sentiment-data.csv",
    hasHeader: true,
    separatorChar: ','
);

// 3. Split data
var dataSplit = mlContext.Data.TrainTestSplit(data, testFraction: 0.2);

// 4. Define pipeline
var pipeline = mlContext.Transforms.Text.FeaturizeText(
        outputColumnName: "Features",
        inputColumnName: "SentimentText")
    .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(
        labelColumnName: "Label",
        featureColumnName: "Features"));

// 5. Train model
var model = pipeline.Fit(dataSplit.TrainSet);

// 6. Evaluate
var predictions = model.Transform(dataSplit.TestSet);
var metrics = mlContext.BinaryClassification.Evaluate(predictions);
Console.WriteLine($"Accuracy: {metrics.Accuracy}");

// 7. Save model
mlContext.Model.Save(model, data.Schema, "sentiment-model.zip");

// 8. Load and use for prediction
var predictionEngine = mlContext.Model.CreatePredictionEngine<SentimentData, SentimentPrediction>(model);
var prediction = predictionEngine.Predict(new SentimentData 
{ 
    SentimentText = "This is great!" 
});
Console.WriteLine($"Prediction: {prediction.Prediction}");

Common ML.NET Tasks

Binary Classification Example

Binary classification is one of the most common ML tasks:

// Define data classes
public class SpamData
{
    [LoadColumn(0)]
    public string Message { get; set; }
    
    [LoadColumn(1)]
    public bool IsSpam { get; set; }
}

public class SpamPrediction
{
    [ColumnName("PredictedLabel")]
    public bool Prediction { get; set; }
    
    public float Probability { get; set; }
    
    public float Score { get; set; }
}

// Training pipeline
var mlContext = new MLContext(seed: 0);
var data = mlContext.Data.LoadFromTextFile<SpamData>("spam-data.csv", hasHeader: true, separatorChar: ',');

var pipeline = mlContext.Transforms.Text.FeaturizeText(
        outputColumnName: "Features",
        inputColumnName: "Message")
    .Append(mlContext.BinaryClassification.Trainers.SdcaLogisticRegression(
        labelColumnName: "IsSpam",
        featureColumnName: "Features"));

var model = pipeline.Fit(data);
var metrics = mlContext.BinaryClassification.Evaluate(
    model.Transform(data), 
    labelColumnName: "IsSpam");

Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:P2}");

Regression Example

public class HouseData
{
    [LoadColumn(0)]
    public float Size { get; set; }
    
    [LoadColumn(1)]
    public float Bedrooms { get; set; }
    
    [LoadColumn(2)]
    public float Price { get; set; }
}

public class HousePrediction
{
    [ColumnName("Score")]
    public float Price { get; set; }
}

var mlContext = new MLContext();
var data = mlContext.Data.LoadFromTextFile<HouseData>("houses.csv", hasHeader: true);

var pipeline = mlContext.Transforms.Concatenate("Features", "Size", "Bedrooms")
    .Append(mlContext.Regression.Trainers.Sdca(
        labelColumnName: "Price",
        featureColumnName: "Features"));

var model = pipeline.Fit(data);
var predictionEngine = mlContext.Model.CreatePredictionEngine<HouseData, HousePrediction>(model);

var prediction = predictionEngine.Predict(new HouseData 
{ 
    Size = 2000, 
    Bedrooms = 3 
});
Console.WriteLine($"Predicted Price: ${prediction.Price:F2}");

Integrating ML.NET with Azure ML

Training Models in Azure ML

You can train ML.NET models in Azure ML for scalability and managed infrastructure:

// Azure ML training script (C#)
using Microsoft.ML;
using Azure.Storage.Blobs;

public class AzureMLTraining
{
    public static void Main(string[] args)
    {
        // Get data from Azure Blob Storage
        var connectionString = Environment.GetEnvironmentVariable("AZURE_STORAGE_CONNECTION_STRING");
        var containerName = args[0]; // Passed from Azure ML job
        
        var blobServiceClient = new BlobServiceClient(connectionString);
        var containerClient = blobServiceClient.GetBlobContainerClient(containerName);
        
        // Download training data
        var blobClient = containerClient.GetBlobClient("training-data.csv");
        var localPath = "training-data.csv";
        blobClient.DownloadTo(localPath);
        
        // Train ML.NET model
        var mlContext = new MLContext();
        var data = mlContext.Data.LoadFromTextFile<ModelData>(localPath, hasHeader: true);
        
        var pipeline = /* ... define pipeline ... */;
        var model = pipeline.Fit(data);
        
        // Save model to Azure ML
        var modelPath = args[1]; // Output path from Azure ML
        mlContext.Model.Save(model, data.Schema, modelPath);
    }
}

// Azure ML Python SDK to submit job
// Python code to submit C# training job
from azure.ai.ml import MLClient, command
from azure.identity import DefaultAzureCredential

ml_client = MLClient(DefaultAzureCredential(), ...)

job = command(
    code="./mlnet-training",
    command="dotnet run --project TrainingProject.csproj",
    environment="dotnet-env:1",
    compute="cpu-cluster",
    inputs={
        "data": Input(type="uri_file", path="azureml://datastores/workspaceblobstore/paths/data/")
    },
    outputs={
        "model": Output(type="uri_folder", path="azureml://datastores/workspaceblobstore/paths/models/")
    }
)

ml_client.jobs.create_or_update(job)

Model Evaluation and Metrics

Understanding Evaluation Metrics

Interview Question: "What metrics would you use to evaluate a binary classification model?"

Answer: For binary classification: Accuracy, Precision, Recall, F1-Score, AUC-ROC. For regression: R-Squared, Mean Absolute Error (MAE), Root Mean Squared Error (RMSE). Choose metrics based on business requirements (e.g., precision for spam detection, recall for disease diagnosis).

// Binary classification metrics
var metrics = mlContext.BinaryClassification.Evaluate(predictions, labelColumnName: "Label");

Console.WriteLine($"Accuracy: {metrics.Accuracy:P2}");
Console.WriteLine($"Precision: {metrics.PositivePrecision:P2}");
Console.WriteLine($"Recall: {metrics.PositiveRecall:P2}");
Console.WriteLine($"F1-Score: {metrics.F1Score:P2}");
Console.WriteLine($"AUC-ROC: {metrics.AreaUnderRocCurve:P2}");

// Confusion matrix
Console.WriteLine($"True Positives: {metrics.ConfusionMatrix.GetCountForClass(1, 1)}");
Console.WriteLine($"False Positives: {metrics.ConfusionMatrix.GetCountForClass(0, 1)}");
Console.WriteLine($"True Negatives: {metrics.ConfusionMatrix.GetCountForClass(0, 0)}");
Console.WriteLine($"False Negatives: {metrics.ConfusionMatrix.GetCountForClass(1, 0)}");

// Regression metrics
var regressionMetrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Price");
Console.WriteLine($"R-Squared: {regressionMetrics.RSquared:P2}");
Console.WriteLine($"MAE: {regressionMetrics.MeanAbsoluteError:F2}");
Console.WriteLine($"RMSE: {regressionMetrics.RootMeanSquaredError:F2}");

Advanced ML.NET Topics

Custom Transformers

Creating custom transformers extends ML.NET capabilities:

// Custom transformer example
public class CustomNormalizer : IEstimator<ITransformer>
{
    private readonly string _inputColumn;
    private readonly string _outputColumn;
    
    public CustomNormalizer(string inputColumn, string outputColumn)
    {
        _inputColumn = inputColumn;
        _outputColumn = outputColumn;
    }
    
    public ITransformer Fit(IDataView input)
    {
        // Calculate statistics
        var stats = CalculateStatistics(input);
        
        return new CustomNormalizerTransformer(stats, _inputColumn, _outputColumn);
    }
    
    public SchemaShape GetOutputSchema(SchemaShape inputSchema)
    {
        return new SchemaShape(new[]
        {
            new SchemaShape.Column(_outputColumn, SchemaShape.Column.Vector.Single)
        });
    }
}

// Usage in pipeline
var pipeline = mlContext.Transforms.CustomMapping(
    (input, output) => { /* custom logic */ },
    contractName: "CustomTransform")
    .Append(/* other transforms */);

Real-World Interview Questions

Question 1: ML.NET vs Python ML Libraries

Answer: ML.NET is ideal for .NET ecosystems, provides better integration with existing .NET codebases, and can run on edge devices. Python libraries (scikit-learn, TensorFlow) offer more algorithms, larger community, and better research support. Choose based on your tech stack and requirements.

Question 2: How do you handle feature engineering in ML.NET?

Answer: ML.NET provides built-in transformers for common feature engineering tasks: text featurization, categorical encoding, normalization, missing value handling. For custom features, create custom transformers or preprocess data before loading into ML.NET.

Behavioral Interview Tips

Discussing ML.NET Experience

Describe projects where you integrated ML into .NET applications
Explain how you handled model versioning and deployment
Share experiences with model performance tuning
Discuss collaboration between .NET developers and data scientists
Talk about challenges in production ML.NET deployments

Conclusion

ML.NET and Azure ML provide powerful tools for .NET developers to implement machine learning solutions. Focus on understanding the ML.NET pipeline, common ML tasks, evaluation metrics, and integration patterns with Azure. Practice building end-to-end ML solutions and be prepared to discuss trade-offs between ML.NET and other ML frameworks.

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