Apache StreamPipes (incubating) is an industrial IoT toolbox which enables non-technical users to flexibly connect, analyze and exploit continuous data streams. Under the hood, StreamPipes integrates an event-driven microservice architecture with a rich graphical user interface that lets users create stream processing pipelines. In this talk, we focus on analyzing image data from industrial cameras with Machine Learning (ML) and Apache StreamPipes. Based on an application example for Visual Quality Inspection, techniques for integrating ML models into StreamPipes are presented and a demo shows how product defects can be easily recognized in real time using a no-code approach. The talk gives an overview of Apache StreamPipes, highlights the positioning of StreamPipes within the ASF IoT ecosystem and demonstrates how machine learning models can be easily integrated and evaluated within a StreamPipes application.
Philipp Zehnder: Philipp Zehnder is a research scientist at the FZI Research Center of Information Technology. His current research interests are in the areas of Distributed Stream Processing and Streaming Machine Learning. He is very interested in open source software, especially in the field of IIoT, and is involved in the Apache StreamPipes (incubation) project.
Marco Heyden: Marco Heyden currently works at the FZI Research Center for Information Technology in Karlsruhe. He is passionate about data stream processing with a focus on unsupervised machine learning and federated learning. He has worked in several public-funded research projects related to Machine Learning and Data Stream Processing in industrial IoT.