Enterprise AI Architectures — Complete Guide
Enterprise AI Architectures — 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 80 of 100
Enterprise AI Architectures
Foundations ✓ → Models ✓ → NLP & advanced → MLOps
NLP & advanced · 3 — Recs, text, ONNX · ~10 min · Module 8: Advanced ML.NET
What is this?
Enterprise AI architectures layer data lakes, feature stores, model registry, and governed inference tiers.
Why should you care?
AIPredict spans fraud, recs, and forecast — architecture must separate batch train from online score.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
// layers: ingest → feature store → train worker → registry → inference API
Console.WriteLine($"TrainWorker → {registryUrl} → InferencePool → MapPost");
var champion = registry.GetChampion("fraud");
services.AddPredictionEnginePool<TxRow, FraudPred>().FromFile(champion.Path);What happened?
- Registry holds champion path; APIs never train.
- Feature store (or SQL views) feeds consistent offline/online features.
Practice next
- Draw ingest/train/infer boxes.
- Champion path from registry.
- Pool loads champion only.
- Add event bus for retrain signals.
- Document data lineage per model.
Remember
Separate train vs infer. Registry champion. Shared features.
AIPredict reference arch
CTO review maps fraud/rec/forecast services.
Outcome: Shared patterns across squads.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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