AI engineering, process optimization

AI Systems Engineering addresses the systematic development and operation of AI-based solutions as part of systems that perform complex tasks.



The design and engineering of complex systems that contain AI and ML components differ from classical engineering, which only uses clearly described components whose behavior can be predicted relatively accurately in advance. Systems with machine learning and decision-making capabilities, on the other hand, may only reveal their final behavior or functionality at runtime, depending on the data. Nevertheless, such systems with intelligent components must be designed in such a way that reliable predictions can be made about their behavior during runtime and guarantees can be given.

AI Systems Engineering, as a discipline complementing basic research into AI methods, makes the use of artificial intelligence systematically accessible and available to engineering. Particularly with regard to the possible certification of AI systems (especially with regard to functional safety, IT security, and privacy), a reliable description of AI and ML components and systems is necessary in order to be able to plan AI and ML components accurately during system design, i.e., at the design stage.

Through the AI Systems Engineering methodology (PAISE® - Process Model for AI Systems Engineering) developed under the leadership of our institute and the use of appropriate tools, we address the systematic development and operation of AI-based solutions as an integral part of complex systems for performing demanding tasks.

Projects

CC-KING: Competence Center AI Systems Engineering

The Karlsruhe Competence Center for AI Systems Engineering bridges the gap between cutting-edge AI research and established engineering disciplines. It conducts fundamental research and develops tools to facilitate the use of artificial intelligence (AI) and machine learning (ML) methods in business practice. Its scope focuses on industrial production and mobility.  

AI Alliance Baden-Württemberg Data Platform

The “Data Platforms” subproject of the AI Alliance Baden-Württemberg lays the technical and organizational foundation for companies and start-ups to gain low-threshold access to data and AI models from others. The aim is to establish a market for data that promotes the development and application of innovative AI solutions. The project is funded by the Baden-Württemberg Ministry of Economic Affairs.

AI Alliance Baden-Württemberg AI Challenge

The aim of the AI Alliance Baden-Württemberg's “AI Challenge” sub-project is to methodically identify areas of application for AI and tap into potential for new business models. To this end, regional workshops bring together expertise and perspectives from different fields. Practical knowledge, scientific approaches, and the experience of users and manufacturers of AI systems are combined to work together on specific challenges. The project is funded by the Baden-Württemberg Ministry of Economic Affairs.

AutoLern

The AutoLern project focuses on drift management for production process data and uses machine learning (ML) to increase the efficiency and longevity of ML models in industrial environments. Data and concept drifts caused by factors such as tool wear or seasonal fluctuations can significantly impair model performance. AutoLern implements both performance-based and distribution-based drift methods for early detection and adaptation to such changes. Through continuous monitoring and automatic model adaptation, the project ensures that ML models remain accurate and adaptable, even in dynamic production environments.

AI Systems Engineering Solutions

 

PAISE®

AI projects in domains such as mobility or industrial production are usually complex, require heterogeneous teams, and carry a high risk of failure. The “Process Model for AI Systems Engineering” describes how they can nevertheless be made a success.

 

Explanatory video about PAISE®

The Process Model for AI Systems Engineering, or PAISE for short, is our process model for the systematic and standardized development and operation of AI-based system solutions. This video explains what it does.

 

CHAISE

From data integration, model development and training to continuous monitoring during operation: CHAISE, the tool chain for AI Systems Engineering, offers a software toolbox for implementing AI applications that is perfectly matched to PAISE.