ONNX Integration — Complete Guide
ONNX Integration — 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 72 of 100
ONNX Integration
Foundations ✓ → Models ✓ → NLP & advanced → MLOps
NLP & advanced · 3 — Recs, text, ONNX · ~10 min · Module 8: Advanced ML.NET
What is this?
ONNX integration loads exported models into ML.NET via ApplyOnnxModel for cross-framework scoring.
Why should you care?
AIPredict image fraud or advanced NLP may come from Python but score inside the .NET API.
See it live — copy this example
Use a .NET console or Web API project with Microsoft.ML. Run dotnet run after pasting.
var onnx = ml.Transforms.ApplyOnnxModel(
modelFile: "models/fraud-resnet.onnx",
outputColumnNames: new[] { "Probability" },
inputColumnNames: new[] { "input" });
var pipe = onnx.Append(ml.Transforms.Concatenate("Features", "Probability"))
.Append(ml.BinaryClassification.Trainers.LbfgsLogisticRegression());
var model = pipe.Fit(placeholderRows);What happened?
- Match ONNX input/output names exactly.
- Often combine ONNX embeddings with ML.NET heads for tabular fusion.
Practice next
- Export ONNX from training tool.
- ApplyOnnxModel with I/O names.
- Evaluate fused pipeline.
- Score pure ONNX without extra head.
- Validate output shape in unit test.
Remember
ApplyOnnxModel bridge. Name alignment. Fuse with tabular.
AIPredict ONNX fraud image
Receipt photo ONNX runs in same API as tabular fraud.
Outcome: Single C# deploy, no Python runtime.
Interview prep for this lesson
Practice these questions aloud after reading—each links to a full structured answer.
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