# Interpretability on AWS
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You can use Jupyter instances that are managed by Amazon SageMaker AI to easily install Python modules through Conda and `pip`. For information about Python packages for SHAP and integrated gradient-based methods, see the [Resources](resources.md) section. For smaller jobs and local testing on a SageMaker AI Jupyter instance, using the methods from these Python packages might be sufficient. If you are using a SageMaker AI managed model, SageMaker AI Clarify provides convenience methods for launching Kernel SHAP on a dedicated instance, and offloading the computation while a model developer continues to work on their Jupyter instance. For more information, see [Create Feature Attribute Baselines and Explainability Reports](https://docs.aws.amazon.com/sagemaker/latest/dg/clarify-feature-attribute-baselines-reports.html) in the SageMaker AI documentation.