Projekt Details



Dr. Stefan Gindl
Dr. Stefan Gindl


The IS RenAIssance platform supports people working in the fields of data science, AI and machine learning in their experiments. It is an alternative to other platforms such as Google Collab. It is also an Austrian solution, which means it is GDPR-compliant. It can provide valuable support in the education sector in particular. Use at highger education facilities is being considered. The platform provides important support for Lower Austria as a business location because it can be used to carry out experiments and software developments and is also GDPR-compliant. The data does not leave Austrian servers.

The platform is available as an open source library and can therefore be used freely. Apart from this, it will also be distributed as part of a cloud solution as software-as-a-service. The intended first customers are educational institutions, such as universities of applied sciences, which can use the platform as part of their courses.

The aim of this project is to further develop a collaborative programming environment for data science and AI applications that has already been built in a preliminary project using open source components. This includes research into the extent to which data visualization can and should be
can and should be integrated, always in line with our client’s needs. The visualization pipeline should support the analysis of large amounts of data, the generation of models and their fine-tuning.

Data science and AI are valuable methods in the areas of production, IoT and Industry5.0, where they help to understand and optimize processes
understand and optimize processes (e.g. predictive maintenance). The platform supports people and institutions in this area through its low-threshold availability. It can be used profitably by educational institutions (e.g. the St. Pölten UAS has a focus on data science) or companies.

Centralized solution instead of local models
When carrying out experiments and software development in the fields of data science, AI, data engineering and machine learning, an approach is often pursued in which iterative prototypes are created and different parameterizations of models are compared with each other. It is therefore worth using a corresponding development environment that is specially adapted to these requirements. Such development environments are often installed locally by employees, i.e. on their own laptops and desktop computers. The data and models are then split up on different computers and are difficult to merge. A central solution therefore makes sense.

A similar platform was developed as part of a preliminary project, which is to be supplemented by another important component in this project. The platform from the preliminary project is to be expanded to include a data visualization pipeline that supports employees in viewing and understanding the data and drawing valuable insights from it. This pipeline should fit seamlessly into the existing system and be integrated into it. An important aspect of the project is the use of existing open source solutions so that the resulting platform has an open character. There are also plans to offer the platform as software-as-a-service. This dual approach serves two needs: on the one hand, it can be used free of charge by people in the sector.

However, this has the disadvantage that the solution then has to be maintained by the user. For more robust applications, such as use in an educational institution, a hosted solution can be accessed for a fee, where no independent hosting or maintenance is required.