Production Pipelines — Complete Guide
Production 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 30 of 100
Production Pipelines
Foundations ✓ → Models → NLP & advanced → MLOps
Models · 2 — Classify & regress · ~6 min · Module 3: ML.NET Pipelines
What is this?
Production pipelines are versioned, tested, and identical between train save and API load.
Why should you care?
AIPredict outages often come from train/serve skew, not the trainer choice.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
// shared AIPredict.Features project referenced by TrainWorker + Api
IEstimator<ITransformer> BuildPipeline(MLContext ml) => Features(ml).Append(Trainer(ml));
// worker: Fit + Save | api: Load + PredictionEnginePoolWhat happened?
- One BuildPipeline.
- Integration test: train tiny set, save, load, predict known row.
- Env var for model path.
Practice next
- Share Features project.
- Save/load round-trip test.
- Model path from config.
- Break a column name on purpose — test should fail.
- Add model version header.
Remember
Shared pipeline code. Round-trip test. Config model path.
AIPredict shared pipeline
Worker and API share BuildPipeline.
Outcome: Skew bugs caught in CI.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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