ML.NET Tutorial
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Tutorials ML.NET Tutorial

AI Microservices — Complete Guide

AI Microservices — Complete Guide: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.

8 min read Updated 8/20/2026
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ML.NET Tutorial · Lesson 85 of 100

AI Microservices

Foundations ✓ → Models ✓ → NLP & advanced ✓ → MLOps

MLOps · 4 — APIs & deploy · ~10 min · Module 9: ASP.NET Core AI Integration

What is this?

AI microservices isolate one model domain per deployable — fraud, recs, forecast — with own lifecycle.

Why should you care?

AIPredict teams ship fraud fixes without redeploying the forecast service.

See it live — copy this example

Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.

// FraudService Program.cs — only fraud types
builder.Services.AddPredictionEnginePool<TxRow, FraudPred>().FromFile("models/fraud.zip");
app.MapPost("/internal/score", (TxRow tx, PredictionEnginePool<TxRow, FraudPred> pool) =>
    Results.Ok(pool.Predict(tx)));
// Gateway routes /api/fraud → FraudService

What happened?

  • Small services own one zip, one pool, one SLA.
  • API gateway aggregates public routes.

Practice next

  1. Split fraud vs forecast projects.
  2. Internal score endpoint.
  3. Gateway route rules.
  4. Add gRPC internal call option.
  5. Separate autoscale rules per service.

Remember

One model per service. Independent deploy. Gateway aggregation.

AIPredict micro split

Fraud v13 deploys without touching RecService.

Outcome: Blast radius limited to fraud pods.

Interview prep for this lesson

Practice these questions aloud after reading—each links to a full structured answer.

Junior Detailed
Explain Concepts in the context of ML.NET.
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define Concepts…
Mid Detailed
What are common mistakes teams make with LLMs when using ML.NET?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define LLMs in p…
Senior Detailed
How would you debug a production issue related to RAG in a ML.NET application?
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define RAG in pl…
Junior Detailed
Describe a real-world scenario where Production mattered in a ML.NET project.
Short answer: Interviewers want a crisp definition, a practical example from your projects, and awareness of trade-offs—not textbook dumps. Explain a bit more How to structure your answer (60–90 seconds) Define Productio…
All course Q&A Practice exams
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ML.NET Tutorial
Course syllabus
Module 1: ML.NET Foundations
Module 2: Machine Learning Basics
Module 3: ML.NET Pipelines
Module 4: Classification Models
Module 5: Regression Models
Module 6: Recommendation Systems
Module 7: NLP with ML.NET
Module 8: Advanced ML.NET
Module 9: ASP.NET Core AI Integration
Module 10: MLOps & Cloud AI
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