The blueprint for AI systems
Data is the foundation
Future software systems comprise information systems, cyber-physical components and AI-based systems. All of these systems need to be developed in the company based on an integral approach.
The goal of AI-based applications is to identify relationships or patterns in data or to automatically classify vast volumes of data. The development of these applications relies on a project structure and expertise that differs from traditional IT solutions. Paying attention to these differences when setting up and implementing a project is crucial for success.
Prof. Dr. Volker Gruhn, Chairman of the Supervisory Board
Four roles for success
In data-driven projects, four roles are crucial to successful implementation. Here, the term ‘role’ defines a set of skills and responsibilities rather than a person. As such, a single individual might take on more than one role in a project or several people could fill out a single role.
It takes the right expertise to implement AI projects with success
Six Steps – a united goal
A solid model for a flexible approach
The process of developing data-driven applications can be subdivided into up to six process steps, depending on the existing data basis. The linear sequence aids the visualisation and description process. Development teams do not choose the most straightforward approach, but rather opt for the strategy that best suits their project. Here you can see what the process looks like.
The minds behind the concept
The idea of this approach was developed by Volker Gruhn, Marc Hesenius, Wilhelm Koop, Ole Meyer and Nils Schwenzfeier: Towards a software engineering process for developing data-driven applications. In "Proceedings of the 7th International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE '19)". IEEE Press, 35–41.
See also: "Of data, roles and models - a blueprint for AI applications" on heise.de.
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