Incremental Learning Stream learning models are created incrementally and are updated continuously. They are suitable for big data applications where real-time response is vital.

Adaptive learning Changes in data distribution harm learning. Adaptive methods are specifically designed to be robust to concept drift changes in dynamic environments.

Resource-wise efficient Streaming techniques efficiently handle resources such as memory and processing time given the unbounded nature of data streams.

Easy to use scikit-multiflow is designed for users with any experience level. Experiments are easy to design, setup, and run. Existing methods are easy to modify and extend.

Stream learning tools In its current state, scikit-multiflow contains data generators, multi-output/multi-target stream learning methods, change detection methods, evaluation methods, and more.

Don't forget to tag @scikit-multiflow in your comment, otherwise they may not be notified.

Authors community post
Share this project
Similar projects
Wallaroo
Distributed Stream Processing to Build and scale real-time applications.
Faust
Python Stream Processing.
StreamAlert
A serverless, realtime data analysis framework which empowers you to ingest, analyze, and alert on data from any environment, using datasources and alerts.
Top collections