Practical Machine Learning for Streaming Data

Heitor Murilo Gomes, Albert Bifet

Abstract: Machine Learning for Data Streams has been an important area of research since the late 1990s, and its use in industry has grown significantly over the last few years. However, there is still a gap between the cutting-edge research and the tools that are readily available, which makes it challenging for practitioners, including experienced data scientists, to implement and evaluate these methods in this complex domain. Our tutorial aims to bridge this gap with a dual focus. We will discuss important research topics, such as partially delayed labeled streams, while providing practical demonstrations of their implementation and assessment using CapyMOA, an open-source library that provides efficient algorithm implementations through a high-level Python API. Source code is available in https://github.com/adaptive-machine-learning/CapyMOA while the accompanying tutorials and installation guide are available in https://capymoa.org/.