> For the complete documentation index, see [llms.txt](https://nx.docs.scailable.net/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://nx.docs.scailable.net/nx-ai-manager-v4.x/for-data-scientists/importing-models/from-scikit-learn.md).

# From Scikit-learn

## About Scikit-learn

![](/files/34dtkRULieIUEJJ3epOZ)

[**scikit-learn**](https://scikit-learn.org/stable/) is a Python module for machine learning built on top of SciPy and is distributed under the 3-Clause BSD license. The project was started in 2007 by David Cournapeau as a Google Summer of Code project, and since then many volunteers have contributed.

Scikit-learn contains a collection of classic ML models that can be used to solve various practical tasks: classification, regression, data points clustering, etc. using for instance SVM, logistic regression, decision tree, isolation forest, etc.

## Model deployment from Scikit-learn

Model deployment from scikit-learn is simple to achieve by exporting your sklearn model to ONNX using the [skl2onnx](https://onnx.ai/sklearn-onnx/) package.

After obtaining a [clean ONNX graph that adheres to our requirements](/nx-ai-manager-v4.x/for-data-scientists/onnx-requirements.md), you can upload it to the [Nx AI cloud for deployment](/nx-ai-manager-v4.x/nx-ai-cloud/upload-your-model.md).


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