Quality Assurance of Machine Learning Applications

The aim of the association is to support SMEs on the implementation of the special machine learning software development lifecycle (ML-SDLC) and the incorporation of important quality indicators.

Together with five SMEs, three Universities of Applied Sciences are developing suitable instruments for evaluating data quality. The focus here is on the representative coverage of the feature space and the evaluation of the quality of the AI ​​model learned in the learning process. This safeguards the product risk of the manufacturer of AI-based products and guarantees the customer a quantified performance of the products with regard to the decisions of the AI.


In Q-AMeLiA, a network of three universities of applied sciences combine their expertise:

  • Prof. Dr.-Ing. Astrid Laubenheimer, Intelligent Systems Research Group (ISRG), Karlsruhe University of Applied Sciences
  • Prof. Dr.-Ing. Janis Keuper, Institute for Machine Learning and Analytics (IMLA), Offenburg University
  • Prof. Dr. Christoph Reich (consortium leader), Institut für Data Science, Cloud Computing und IT-Sicherheit (IDACUS) Furtwangen University


The research results are transferred to the private sector in close cooperation with the project partners:

  • competition it-management GmbH
  • C.R.S. iiMotion GmbH
  • tepcon GmbH
  • Inferics GmbH
  • schrempp edv GmbH

Further Information


Project Coordinator:
Prof. Dr. Astrid Laubenheimer (for HKA)

Funding: Ministry of Science, Research and Arts of Baden-Württemberg (MWK)

Duration: 2020 - 2023