Real-Time Predictions — Complete Guide
Real-Time Predictions — 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 78 of 100
Real-Time Predictions
Foundations ✓ → Models ✓ → NLP & advanced → MLOps
NLP & advanced · 3 — Recs, text, ONNX · ~10 min · Module 8: Advanced ML.NET
What is this?
Real-time predictions score individual events as they arrive with low latency and warm engines.
Why should you care?
AIPredict must flag suspicious checkout before payment capture completes.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
services.AddPredictionEnginePool<TxRow, FraudPred>()
.FromFile("models/fraud-v12.zip", watchForChanges: true);
app.MapPost("/api/fraud/score", (TxRow tx, PredictionEnginePool<TxRow, FraudPred> pool) =>
{
var sw = Stopwatch.StartNew();
var p = pool.Predict(tx);
return Results.Ok(new { p.Probability, ms = sw.ElapsedMilliseconds });
});What happened?
- PredictionEnginePool warms models and supports reload.
- Log latency per request; block if p95 exceeds SLA.
Practice next
- Pool with watchForChanges.
- MapPost single-row predict.
- Log elapsed ms.
- Pre-warm pool at startup.
- Reject if ms > 200.
Remember
Warm pool. Single-row path. Latency logging.
AIPredict checkout score
Payment flow waits for fraud probability.
Outcome: Capture blocked when p>0.92 under 80ms.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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