jubatus

Jubatus is an open-source framework for real-time, online machine learning on streaming data. It provides a collection of algorithms that can learn incrementally from new examples, enabling models to adapt continuously without retraining from scratch. Jubatus is designed for low-latency scoring on high-volume data, making it suitable for real-time analytics, fraud detection, recommendation, and monitoring tasks.

The system is designed as a distributed framework that can run on clusters of machines. Data is fed through client APIs, features are computed, and instances are streamed to model servers that maintain online models. The architecture supports horizontal scaling by adding more worker nodes, and it combines predictions from multiple partitions as needed. Jubatus can operate with various data flows and uses a lightweight protocol for coordinating model updates and predictions.

The framework includes a range of online learning algorithms for tasks such as classification, regression, clustering, and anomaly detection. Models are updated incrementally using new data, allowing immediate adaptation to changing patterns. Jubatus provides client libraries for several programming languages, including Python, Ruby, Java, and C++, to facilitate integration with applications.

Jubatus has been used in industry for real-time scoring in web services, streaming analytics, and security monitoring. It is typically deployed as part of a larger data processing stack and can be integrated with messaging systems and data stores through its APIs and connectors. Documentation covers installation, tutorials, and examples of common workflows.