Feature Pipelines — Complete Guide
Feature Pipelines — 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 22 of 100
Feature Pipelines
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 3: ML.NET Pipelines
What is this?
Feature pipelines are the Estimator chain that builds Features from raw columns.
Why should you care?
AIPredict keeps feature pipelines in code next to the trainer so train and serve match.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
IEstimator<ITransformer> Features() =>
ml.Transforms.Categorical.OneHotEncoding("Cat", "MerchantCategory")
.Append(ml.Transforms.NormalizeMinMax("AmountN", "Amount"))
.Append(ml.Transforms.Concatenate("Features", "AmountN", "Hour", "Cat"));What happened?
- Share the same method for training and for documentation.
- Saved model embeds the fitted transforms.
Practice next
- Extract Features() helper.
- Append trainer.
- Fit once and save.
- Add missing-value replace.
- Unit-test schema names.
Remember
One feature function. Saved transforms. Train=serve.
AIPredict shared Features()
Train and docs use one helper.
Outcome: No train/serve skew from encoding.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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