CapyMOA让数据流和在线持续学习更高效，支持实时模型演化。
CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python
- 基于模块化架构，融合MOA、scikit-learn与PyTorch
- 支持动态数据中模型的自适应更新与持续学习
- 适合需要实时处理的工业级流数据场景
CapyMOA 是一个开源的 Python 库，用于高效处理数据流和在线持续学习。它提供结构化框架，支持模型在时间推移中自适应演化。其架构可与 MOA、scikit-learn 及 PyTorch 等框架集成，实现高性能在线算法与现代深度学习技术的结合。通过强调效率、可扩展性和易用性，CapyMOA 使研究人员和实践者能够应对跨多个领域的动态学习挑战。官网：https://capymoa.org。GitHub：https://github.com/adaptive-machine-learning/CapyMOA。
原文摘要 · Abstract (English)
CapyMOA is an open-source Python library for efficient machine learning on data streams and online continual learning. It provides a structured framework for real-time learning, supporting adaptive models that evolve over time. CapyMOA's architecture allows integration with frameworks such as MOA, scikit-learn and PyTorch, enabling the combination of high-performance online algorithms with modern deep learning techniques. By emphasizing efficiency, scalability, and usability, CapyMOA allows researchers and practitioners to tackle dynamic learning challenges across various domains. Website: https://capymoa.org. GitHub: https://github.com/adaptive-machine-learning/CapyMOA.
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