ML.NET with ASP.NET Core — Complete Guide
ML.NET with ASP.NET Core — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.
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ML.NET Tutorial · Lesson 81 of 100
ML.NET with ASP.NET Core
Foundations ✓ → Models ✓ → NLP & advanced ✓ → MLOps
MLOps · 4 — APIs & deploy · ~10 min · Module 9: ASP.NET Core AI Integration
What is this?
ML.NET with ASP.NET Core registers models at startup and serves predictions through DI-friendly pools.
Why should you care?
AIPredict production APIs are ASP.NET Core — ML.NET must plug into Program.cs cleanly.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
builder.Services.AddPredictionEnginePool<TxRow, FraudPred>()
.FromFile(builder.Configuration["Models:FraudPath"]!, watchForChanges: true);
var app = builder.Build();
app.MapPost("/score", (TxRow tx, PredictionEnginePool<TxRow, FraudPred> pool) =>
Results.Ok(pool.Predict(tx)));
app.Run();What happened?
- AddPredictionEnginePool in DI, inject into minimal APIs or controllers.
- Configuration drives model path per environment.
Practice next
- Add Microsoft.ML.Extensions package.
- Register pool FromFile.
- MapPost inject pool.
- Move path to appsettings.Production.json.
- Add IHostedService warm-up.
Remember
DI pool registration. Config model path. Minimal API inject.
AIPredict ASP.NET wire-up
Fraud API uses standard Program.cs pattern.
Outcome: Team ships scores like any other endpoint.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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