Project partners develop framework for development, operation and maintenance of AI-based software
News from May 03, 2021
The IML4E project started on May 1, 2020. Led by Fraunhofer FOKUS, the project partners from the European software industry want to develop a European framework for the development, operation and maintenance of AI-based software. In the project, Fraunhofer FOKUS is researching techniques, tools and methods that can be used to systematically document and model the uncertainty and risks in machine learning due to data quality issues.
Artificial intelligence (AI) and machine learning (ML) are increasingly becoming the basis for intelligent services (smart services), such as predictive maintenance, or intelligent software (smart software), such as for route planning in road traffic. For such new applications to be developed, AI- and ML-based software must be able to be produced, operated and maintained with similar efficiency and quality as "classic" software. Suitable methods, tools and processes are needed for this.
The IML4E project has therefore brought together companies from the main sectors of the European software industry to develop a European framework for the development, operation and maintenance of AI-based software to ensure the development of intelligent services and intelligent software on an industrial scale.
Dr. Jürgen Großmann, project head at Fraunhofer FOKUS, explains: "With the IML4E project, we want to strengthen the European software industry to be able to efficiently apply advantages of Artificial Intelligence and Machine Learning without having to take unnecessary quality risks."
Fraunhofer FOKUS is leading the overall European project IML4E. In the project, the scientists develop techniques, tools and methods to systematically document and model the uncertainty and risks in machine learning due to data quality problems. For this purpose, quality features and risk-based testing approaches will be developed that are suitable for continuous quality monitoring throughout the lifecycle of AI-based software. In another work package, methods and techniques that can be used to establish transparency and comprehensibility of machine learning models will be developed. Fraunhofer FOKUS is developing a risk-based quality assurance approach with which machine learning software can be tested for properties such as security and robustness and can thus also be used in critical application areas. In addition, a teaching and experimentation platform, as well as suitable teaching and training material for industrial users, is being created.
In the ITEA3 project IML4E, 18 partners from Finland, Hungary and Germany are working together. The project is funded by the German Federal Ministry of Education and Research (BMBF).