Q-AMeLiA

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Software development for companies

Nowadays machine learning (ML) engineering and development is carried out and implemented in almost every organisation to a certain extent - in a very analog way similar to the way software development has always taken place. However, the distinct characteristics of ML software mean it differs significantly from traditional company software development:

  • models are based on historical data, while the data itself are constantly changing
  • the quality of the data is taken as a given, without appropriate testing and validation
  • summaries of performance (accuracy, precision, F1 result) for particular data sets provide no guarantee of generalised performance for data from the real world
  • attempts at integration are generally underestimated and often simply entail the insertion of models into downstream processes, with the result that inflated or failed integrations have to be manually manipulated
  • constant further development of tools and techniques in the ML ecosystem results in a multitude of moving parts.

The aim of the joint project is to support SMEs with the special ML software development lifecycle (ML-SDLC) and the significant quality indicators which result from this. Five SMEs will work with three universites of applied sciences to develop evaluation of data quality related to representative coverage of the characterisation, as well as evaluation of AI-model quality gained during the learning process. This will ensure the product risk of the manufacturers' AI-based products and guarantee the customer quantified performance related to AI decisions.

  • ML software has distinct characteristics Machine learning engineering is still developed and implemented similarly to software development although its distinct characteristics make this unsuitable.
  • ML software development lifecycle (ML-SDLC) support SMEs and universities are cooperating to support SMEs with the ML software development lifecycle (ML-SDLC) and the significant quality indicators which result from this.
  • Reduced product risk and guaranteed performance Evaluation of data and AI-model quality gained during the learning process will reduce product risk and guarantee customers quantified AI-based product performance.

Project partners

  • Offenburg University
  • Karlsruhe University of Applied Sciences
  • Competion IT GmbH
  • Inferics GmbH
  • C.R.S. iiMotion GmbH
  • tepcon GmbH
  • schrempp edv GmbH

Funding

Ministry of Science, Research and the Arts, Baden-Württemberg

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