ML.NET Workflow — Complete Guide
ML.NET Workflow — 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 10 of 100
ML.NET Workflow
Foundations → Models → NLP & advanced → MLOps
Foundations · 1 — Context & data · ~6 min · Module 1: ML.NET Foundations
What is this?
The standard workflow: load → transform → train → evaluate → save → load → predict.
Why should you care?
AIPredict teams share this checklist so labs and production stay aligned.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var model = pipeline.Fit(train);
var metrics = ml.BinaryClassification.Evaluate(model.Transform(test));
ml.Model.Save(model, train.Schema, "models/fraud.zip");
var loaded = ml.Model.Load("models/fraud.zip", out var schema);
var engine = ml.Model.CreatePredictionEngine<TxRow, FraudPred>(loaded);What happened?
- Never skip Evaluate.
- Save schema with the model.
- Inference only needs Load + PredictionEngine.
Practice next
- Fit on train.
- Evaluate on test.
- Save zip.
- Print metrics.AreaUnderRocCurve.
- Predict two sample txs.
Remember
Load→train→eval→save→predict. Evaluate always. Zip includes transforms.
AIPredict fraud.zip workflow
Lab produces a reusable model file.
Outcome: API can load the same zip.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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