AI Engineering

Vision and Mission

Artificial intelligence (AI) and machine learning (ML) are increasingly broadly adopted in industry, However, in our research we have learned that deploying industry-strength, production quality ML models in systems proves to be challenging. Companies experience challenges related to data quality, design methods and processes, performance of models as well as deployment and compliance. To address this, a new, structured engineering approach is required to construct and evolve systems that contain ML/DL components. We refer to this as AI Engineering, i.e. an extension of Software Engineering with new processes and technologies needed for development and evolution of AI systems, i.e. systems that include AI components.

During the last year, in Software Center, we have built up a team of 10 people working on AI engineering, funded by Vinnova, WASP and CHAIR. An overview of the research activities is shown in the figure below. Currently, we conduct research federated learning, DataOps, automatic labelling, A/B testing of models, monitoring & logging, transfer learning, heterogeneous hardware, automated experimentation and autonomously improving systems. We are always looking for more companies to become involved, so please reach out in case you want to learn more and get involved.

Presentations from the first AI Engineering Workshop, August 2020 Presentations from the first AI Engineering Workshop 27 August 2020 are available on the Software Center intranet. As a partner you have access to the Software Center intranet, where you find the documentation from the workshop. Log in with your e-mail to read more:

Theme 5 Leader: Jan Bosch
Jan Bosch


Jan Bosch, Professor, Software Engineering, Department of Computer Science and Engineering