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

TL;DR

CapyMOA is an open-source Python library that enables efficient, scalable, and real-time machine learning on data streams and online continual learning, integrating multiple frameworks for versatile applications.

Contribution

It introduces a structured framework supporting adaptive models and seamless integration with MOA, scikit-learn, and PyTorch for dynamic learning tasks.

Findings

Supports real-time learning on data streams

Enables integration with deep learning frameworks

Offers scalable and efficient online algorithms

Abstract

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.