Machine Learning for Streaming Data


Machine learning for data streams (MLDS) has been a significant research area since the late 90s, with increasing adoption in industry over the past few years. Despite commendable efforts in opensource libraries, a gap persists between pioneering research and accessible tools, presenting challenges for practitioners, including experienced data scientists, in implementing and evaluating methods in this complex domain. Our tutorial addresses this gap with a dual focus. We discuss advanced research topics such as partially delayed labeled streams while providing practical demonstrations of their implementation and assessment using Python. By catering to both researchers and practitioners, this tutorial aims to empower users in designing, conducting experiments, and extending existing methodologies.

To access the slides, please join the Discord server: (Discord channel][] or reach out to heitor.gomes at