awesome/machine-learning
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* [Shapash](https://github.com/MAIF/shapash) : Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand. * [Shapash](https://github.com/MAIF/shapash) : Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand.
* [Eurybia](https://github.com/MAIF/eurybia): Eurybia monitors data and model drift over time and securizes model deployment with data validation. * [Eurybia](https://github.com/MAIF/eurybia): Eurybia monitors data and model drift over time and securizes model deployment with data validation.
* [Colossal-AI](https://github.com/hpcaitech/ColossalAI): An open-source deep learning system for large-scale model training and inference with high efficiency and low cost. * [Colossal-AI](https://github.com/hpcaitech/ColossalAI): An open-source deep learning system for large-scale model training and inference with high efficiency and low cost.
* dirty_cat](https://github.com/dirty-cat/dirty_cat) - facilitates machine-learning on dirty, non-curated categories. It provides transformers and encoders robust to morphological variants, such as typos.
* [Upgini](https://github.com/upgini/river): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features. * [Upgini](https://github.com/upgini/river): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your training dataset with only the accuracy improving features.
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