MLPro 2.0 - Online machine learning in Python

In this paper, we present version 2.0 of the open-source middleware MLPro for applied machine learning in Python. Notably, it introduces the new sub-framework MLPro-OA for online machine learning, focusing on standards and templates for classic and online-adaptive data stream processing (DSP/OADSP). As part of this, we provide three novel adaptation mechanisms:The first, event-oriented adaptation, enables localized, event-driven parameter updates within individual tasks. The second, cascaded adaptation, allows adaptation events to propagate across multiple dependent tasks, creating task-spanning adjustment cascades decoupled from the forward-facing DSP. The third, reverse adaptation, allows tasks to revise prior adjustments by explicitly processing obsolete instances discarded from a preceding sliding window. Furthermore, we provide insights into the underlying design criteria of MLPro-OA, which were developed through extensive requirements engineering. In the practical part of this work, we demonstrate the essential functionalities of MLPro-OA using reproducible examples. • Advanced online-adaptation mechanisms: event-oriented, cascaded, reverse adaptation. • Requirements engineering and evaluation of online machine learning (OML) frameworks. • Novel OML framework MLPro-OA for the Python programming language. • Comprehensive examples demonstrating the practical use of MLPro-OA.