ML.NET Tutorial
Lesson 96/100

Tutorials ML.NET Tutorial

AI Monitoring — AIPredict Project

AI Monitoring — 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 8/21/2026
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ML.NET Tutorial · Lesson 96 of 100

AI Monitoring

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

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

What is this?

AI monitoring watches live prediction quality proxies — approval rates, score drift, and error budgets.

Why should you care?

AIPredict fraud team detects when live block rate diverges from training expectations.

See it live — copy this example

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

var live = await db.FraudScores.Where(s => s.Day >= DateTime.UtcNow.AddDays(-1)).ToListAsync();
var avgP = live.Average(s => s.Probability);
var blocked = live.Count(s => s.Probability >= 0.9) / (double)live.Count;
logger.LogInformation("fraud avgP={Avg:P2} blockRate={Block:P2}", avgP, blocked);

What happened?

  • Log aggregate score stats daily.
  • Compare to training baseline; alert on sudden block-rate jumps.

Practice next

  1. Aggregate last-24h scores.
  2. Log avgP and block rate.
  3. Alert vs baseline band.
  4. Histogram buckets in Application Insights.
  5. Compare weekday vs weekend.

Remember

Score aggregates. Baseline compare. Alert on shift.

AIPredict live monitors

Block rate doubles after merchant change.

Outcome: Team investigates before chargeback spike.

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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