Azure ML — AIPredict Project
Azure ML — 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 94 of 100
Azure ML
Foundations ✓ → Models ✓ → NLP & advanced ✓ → MLOps
MLOps · 4 — APIs & deploy · ~10 min · Module 10: MLOps & Cloud AI
What is this?
Azure ML can train and register models in the cloud while ML.NET apps download and score locally.
Why should you care?
AIPredict data science uses Azure ML for heavy training; production API stays ML.NET on App Service.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var amlModel = await registry.GetModelAsync("aipredict-fraud", "12");
await amlModel.DownloadAsync("models/fraud-v12.zip");
var ml = new MLContext();
var model = ml.Model.Load("models/fraud-v12.zip", out _);
services.AddPredictionEnginePool<TxRow, FraudPred>().FromFile("models/fraud-v12.zip");
What happened?
- Train/register in Azure ML workspace; download registered artifact to inference host.
- Keeps .NET runtime at edge.
Practice next
- Register model in AML workspace.
- Download zip to API host.
- Pool FromFile local path.
- Pipeline trigger retrain on new data.
- Compare AML metrics to local eval.
Remember
AML train/register. Download to .NET. Local pool infer.
AIPredict AML handoff
DS trains in AML; API downloads v12.
Outcome: Heavy GPU train, light CPU infer.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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