CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 11 Feb 2025.
Builds on
5 of 12 references.
References · 5
- 2019Gradient based sample selection for online continual learningcited by 571
- 2020River: machine learning for streaming data in Pythoncited by 98
- 2018Scikit-Multiflow: A Multi-output Streaming Frameworkcited by 70
- 2023Avalanche: A PyTorch Library for Deep Continual Learningcited by 33
- 2023Adaptive Bernstein Change Detector for High-Dimensional Data Streamscited by 4
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.
- 2026Rethinking Tabular Foundation Models On Data Streams4 Oct 2026
- 2025DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts24 Jul 2025