# CapyMOA: Efficient Machine Learning for Data Streams and Online Continual Learning in Python

arXiv: https://arxiv.org/abs/2502.07432 · HTML: https://mlsanity.com/paper/2502.07432/

## 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 (most cited first, 5 of 5)

- [Gradient based sample selection for online continual learning](https://mlsanity.com/paper/1903.08671/) (2019)
- [River: machine learning for streaming data in Python](https://mlsanity.com/paper/2012.04740/) (2020)
- [Scikit-Multiflow: A Multi-output Streaming Framework](https://mlsanity.com/paper/1807.04662/) (2018)
- [Avalanche: A PyTorch Library for Deep Continual Learning](https://mlsanity.com/paper/2302.01766/) (2023)
- [Adaptive Bernstein Change Detector for High-Dimensional Data Streams](https://mlsanity.com/paper/2306.12974/) (2023)

## Built on by (most cited first, 2 of 2)

- [DriftMoE: A Mixture of Experts Approach to Handle Concept Drifts](https://mlsanity.com/paper/2507.18464/) (2025)
- [Rethinking Tabular Foundation Models On Data Streams](https://mlsanity.com/paper/2610.05352/) (2026)

## 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.