ML.NET Tutorial
Lesson 95/100

Tutorials ML.NET Tutorial

AI CI/CD — AIPredict Project

AI CI/CD — AIPredict Project: free step-by-step lesson with examples, common mistakes, and interview tips — part of ML.NET Tutorial on Toolliyo Academy.

8 min read Updated 8/22/2026
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ML.NET Tutorial · Lesson 95 of 100

AI CI/CD

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

MLOps · 4 — APIs & deploy · ~10 min · Module 10: MLOps & Cloud AI

What is this?

AI CI/CD runs train-evaluate gates in pipelines before model zips merge to deploy branches.

Why should you care?

AIPredict blocks fraud models that regress AUC below the champion in pull request builds.

See it live — copy this example

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

# azure-pipelines.yml step
- script: dotnet run --project AIPredict.Train -- --out models/ci-fraud.zip
- script: dotnet test AIPredict.ModelTests -- --model models/ci-fraud.zip --min-auc 0.85
- publish: models/ci-fraud.zip
  artifact: fraud-model

What happened?

  • Pipeline trains (or loads cached data), evaluates, fails if metrics below threshold, publishes artifact.
  • Follow the steps below — typing the code yourself is the fastest way to learn.

Practice next

  1. Train step in pipeline.
  2. ModelTests with min AUC.
  3. Publish zip artifact.
  4. Add data hash check step.
  5. Deploy artifact to staging slot.

Remember

Metric gates in CI. Artifact publish. Fail on regression.

AIPredict CI gate

PR drops AUC 0.87→0.82.

Outcome: Pipeline fails; merge blocked.

Interview prep for this lesson

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

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…
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…
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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