onnxruntime/docs/tutorials/inferencing/traditional-ml.md

---
title: Deploy traditional ML
grand_parent: Tutorials
parent: Inferencing
has_children: false
nav_order: 8
---

# Deploy traditional ML models
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ONNX Runtime supports [ONNX-ML](https://github.com/onnx/onnx/blob/master/docs/Operators-ml.md) and can run traditional machine models created from libraries such as Sciki-learn, LightGBM, XGBoost, LibSVM, etc. 

## Contents
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## Convert model to ONNX
* [Scikit-learn conversion](http://onnx.ai/sklearn-onnx/tutorial_1_simple.html)
* [Scikit-learn custom conversion](http://onnx.ai/sklearn-onnx/tutorial_2_new_converter.html)
* [XGBoost conversion](http://onnx.ai/sklearn-onnx/auto_tutorial/plot_gexternal_xgboost.html)
* [LightGBM conversion](http://onnx.ai/sklearn-onnx/auto_tutorial/plot_gexternal_lightgbm.html)
* [ONNXMLTools samples](https://github.com/onnx/onnxmltools/tree/master/docs/examples)

## Deploy model
* *[COMING SOON]* Deploy a Python-trained model in a C# environment
* *[COMING SOON]* Deploy a scikit-learn model securely without pkl files