Components
Dealing with uncertainties of Machine Learning components
The use of Machine Learning (ML) components in safety-critical or financially critical systems is challenging. At Fraunhofer IESE, we address this challenge by systematically engineering comprehensive multi-layered safety concepts and explicitly considering sources of uncertainties. This specifically includes situations at runtime for which ML components were not trained. In this blog post, we present the […]
Realizing Agricultural Data Spaces with Eclipse Dataspace Components (EDC): a Demo Use Case
As is the case in many domains today, data in the agricultural domain is fragmented, stored in disparate silos that have private standards and closed architectures. The lack of interoperability and trust resulting from such a setup hinders the sharing of data, which is necessary to unlock its full potential. One promising solution that aims […]