AI Scaling — AIPredict Project
AI Scaling — AIPredict Project: 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 99 of 100
AI Scaling
Foundations ✓ → Models ✓ → NLP & advanced ✓ → MLOps
MLOps · 4 — APIs & deploy · ~10 min · Module 10: MLOps & Cloud AI
What is this?
AI scaling adds inference replicas, queues heavy batch work, and right-sizes CPU vs GPU tiers.
Why should you care?
AIPredict forecast batch job and fraud API have different scale profiles.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
builder.Services.AddPredictionEnginePool<TxRow, FraudPred>()
.FromFile(modelPath)
.Services.AddOptions<PredictionEnginePoolOptions>()
.Configure(o => o.MaxObjects = Environment.ProcessorCount * 2);
// HPA: kubectl autoscale deployment fraud-api --cpu-percent=70 --min=2 --max=20What happened?
- Tune pool MaxObjects per core; HPA on API deployments; offload batch Transform to worker queue.
- Follow the steps below — typing the code yourself is the fastest way to learn.
Practice next
- Set pool MaxObjects.
- HPA min/max pods.
- Queue batch scoring worker.
- Scale on custom latency metric.
- Separate worker pool for Transform.
Remember
Pool size tuning. HPA for API. Queue for batch.
AIPredict scale out
Fraud API scales 2→15 pods on load.
Outcome: p95 flat while RPS 8x.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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