Real-Time AI Predictions — Complete Guide
Real-Time AI 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 84 of 100
Real-Time AI Predictions
Foundations ✓ → Models ✓ → NLP & advanced ✓ → MLOps
MLOps · 4 — APIs & deploy · ~10 min · Module 9: ASP.NET Core AI Integration
What is this?
Real-time AI predictions prioritize sub-second scoring with pooling, caching, and horizontal scale.
Why should you care?
AIPredict recommendation and fraud endpoints share SLA targets under peak holiday traffic.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
app.MapGet("/api/recs/live/{userId}", async (uint userId, IRecService rec, IMemoryCache cache) =>
{
var key = $"recs:{userId}";
if (!cache.TryGetValue(key, out int[]? ids))
{
ids = await rec.RankTopNAsync(userId, 10);
cache.Set(key, ids, TimeSpan.FromMinutes(5));
}
return Results.Ok(ids);
});What happened?
- Combine pool predict with short TTL cache for hot users.
- Scale pods; keep models local to each pod.
Practice next
- Cache top-N per user 5 min.
- Pool score inside RecService.
- Load-test p95.
- Shorter cache TTL for VIP users.
- Add circuit breaker on slow predict.
Remember
Pool + cache. Horizontal pods. p95 budget.
AIPredict peak traffic
Black Friday rec endpoint holds p95.
Outcome: Cache cuts MF calls 80%.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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