AI Monitoring Systems — Complete Guide
AI Monitoring Systems — 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 89 of 100
AI Monitoring Systems
Foundations ✓ → Models ✓ → NLP & advanced ✓ → MLOps
MLOps · 4 — APIs & deploy · ~10 min · Module 9: ASP.NET Core AI Integration
What is this?
AI monitoring systems track latency, error rates, and prediction volume for each model endpoint.
Why should you care?
AIPredict on-call needs alerts when fraud API errors spike after a deploy.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
app.Use(async (ctx, next) =>
{
var sw = Stopwatch.StartNew();
await next();
sw.Stop();
telemetry.TrackMetric("fraud.predict.latency_ms", sw.ElapsedMilliseconds);
telemetry.TrackMetric("fraud.predict.status", ctx.Response.StatusCode);
});What happened?
- Middleware or OpenTelemetry exporters capture latency and status.
- Tag metrics with model version dimension.
Practice next
- Add latency middleware.
- Track status code metric.
- Tag model version dimension.
- Alert p95 > 150ms.
- Dashboard per model version.
Remember
Latency + errors. Version tags. Alert on spikes.
AIPredict API monitors
Deploy v13 raises 500 rate.
Outcome: Pager fires within 2 minutes.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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