# Resources for using Scikit-learn with Amazon SageMaker AI
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You can use Amazon SageMaker AI to train and deploy a model using custom Scikit-learn code. The SageMaker AI Python SDK Scikit-learn estimators and models and the SageMaker AI open-source Scikit-learn containers make writing a Scikit-learn script and running it in SageMaker AI easier. The following section provides reference material you can use to learn how to use Scikit-learn with SageMaker AI.

**Requirements**

Scikit-learn 1.4 has the following dependencies.


| Dependency | Minimum version | 
| --- | --- | 
| Python | 3.10 | 
| NumPy | 2.1.0 | 
| SciPy | 1.15.3 | 
| joblib | 1.5.2 | 
| threadpoolctl | 3.6.0 | 

The SageMaker AI Scikit-learn container supports the following Scikit-learn versions.


| Supported Scikit-learn version | Minimum Python version | 
| --- | --- | 
| 1.4-2 | 3.10 | 
| 1.2-1 | 3.8 | 
| 1.0-1 | 3.7 | 
| 0.23-1 | 3.6 | 
| 0.20.0 | 2.7 or 3.4 | 

For general information about writing Scikit-learn training scripts and using Scikit-learn estimators and models with SageMaker AI, see [Using Scikit-learn with the SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable/using_sklearn.html).

## What do you want to do?
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**Note**  
Matplotlib v2.2.3 or newer is required to run the SageMaker AI Scikit-learn example notebooks.

I want to use Scikit-learn for data processing, feature engineering, or model evaluation in SageMaker AI.  
For a sample Jupyter notebook, see [https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker\$1processing/scikit\$1learn\$1data\$1processing\$1and\$1model\$1evaluation](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker_processing/scikit_learn_data_processing_and_model_evaluation).  
For a blog post on training and deploying a Scikit-learn model, see [Amazon SageMaker AI adds Scikit-Learn support](https://aws.amazon.com/blogs/machine-learning/amazon-sagemaker-adds-scikit-learn-support/).  
For documentation, see [ReadTheDocs](https://sagemaker.readthedocs.io/en/stable/amazon_sagemaker_processing.html#data-pre-processing-and-model-evaluation-with-scikit-learn).

I want to train a custom Scikit-learn model in SageMaker AI.  
For a sample Jupyter notebook, see [https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/scikit\$1learn\$1iris](https://github.com/awslabs/amazon-sagemaker-examples/tree/master/sagemaker-python-sdk/scikit_learn_iris).  
For documentation, see [Train a Model with Scikit-learn](https://sagemaker.readthedocs.io/en/stable/using_sklearn.html#train-a-model-with-sklearn).

I have a Scikit-learn model that I trained in SageMaker AI, and I want to deploy it to a hosted endpoint.  
For more information, see [Deploy Scikit-learn models](https://sagemaker.readthedocs.io/en/stable/using_sklearn.html#deploy-sklearn-models).

I have a Scikit-learn model that I trained outside of SageMaker AI, and I want to deploy it to a SageMaker AI endpoint  
For more information, see [Deploy Endpoints from Model Data](https://sagemaker.readthedocs.io/en/stable/using_sklearn.html#deploy-endpoints-from-model-data).

I want to see the API documentation for [Amazon SageMaker Python SDK](https://sagemaker.readthedocs.io/en/stable) Scikit-learn classes.  
For more information, see [Scikit-learn Classes](https://sagemaker.readthedocs.io/en/stable/sagemaker.sklearn.html).

I want to see information about SageMaker AI Scikit-learn containers.  
For more information, see [SageMaker Scikit-learn Container GitHub repository](https://github.com/aws/sagemaker-scikit-learn-container).