Matching your business needs, KIT experts evaluate potential new applications of state-of-the-art machine learning algorithms and data science methods based on your existing data sets. After providing data (according to a strict confidentiality agreement), our specialists analyze and evaluate novel approaches against baselines and outline business models as well as development risks based on the results.
Together with KIT experts, your data will be analyzed in four steps, starting with an initial meeting, and the business potentials gained will be condensed. In order to achieve this we are work using state of the art machine learning tools and infrastructure.
• During the initial meeting, the actual objective of the potential analysis is worked out in an on site workshop.
• Based on this, the underlying data set is clarified in terms of its significance with regard to the target definition and, if necessary, additional data required is pointed out. The basis for data delivery is a confidentiality agreement, which includes, among other things, the purpose and the period of use of the data.
• KIT data specialists will then take care of your data and show data strategies in the context of exploratory data analysis as well as application possibilities of modern machine learning algorithms in an interim presentation. In addition, methods will be presented that can be used to exploit potentials.
• KIT data specialists will then examine your data in the manner discussed and evaluate the algorithms applied in order to show you the potentials in the final presentation and provide you with the data as well as recommendations for action.
To deepen your newly acquired knowledge, you will have the opportunity to participate in data analysis training workshops led by KIT experts, whose contents are always up-to-date to develop products from potentials.
The tasks are performed by experienced ML researchers of KIT. KIT is "The Research University in the Helmholtz Association". As one of the largest scientific institutions in Europe, Germany's only university of excellence with national large-scale research facilities combines a long university tradition with program-oriented cutting-edge research. Since KIT has a strong focus on innovation and technology transfer, our experts have many years of experience from applied industrial projects that contribute to our joint success.
SPECIAL ACCESS CONDITIONS
Conditions and requirements for participation in an experiment within the Open Calls:
By participating in an EUHubs4Data Open Call, you are initially only applying for funding that originally comes from the European Commission and is awarded by the coordinator exclusively in its own name under the conclusion of a sub-grant agreement. This sub-grant agreement does not establish a contract with KIT, neither through your application nor through a possible positive funding decision.
KIT will therefore - also in your own interest - conclude a separate written agreement with you at the start of the experiment (based on our sample cooperation agreement: https://www.sdil.de/en/euhubs4data/sdil-model-cooperation-agreement). If you decide to propose the participation of KIT and SDIL infrastructure in your experiment, you must respect the following conditions: https://www.sdil.de/en/euhubs4data/sdil-terms. We provide this information in advance to ensure maximum transparency: please contact us if you have any questions. In the unlikely event that you are unable to conduct your experiment with our participation, we will attempt to assist you in selecting alternative services before the experiment begins.
Please note that contrary to the name "service", the above description is not a genuine commercial offer, but a listing of exclusive contributions as part of a genuine eye-to-eye collaboration.
For genuine commercial offerings related to the above topics, please feel free to contact us any time outside of the Open Calls.
Users need to ensure data availability and should be able to provide related domain knowledge
Typically companies aproach us after collecting data sets from industrial production, eg. from PLCs or MES system and what to correlate that data e.g. to quality control data in order to predict important influence to their product outcome. In other cases e.g. sales data is used together with external data sources to better plan future production. We then help companies to model their business needs as a machine learning problem and analyze the potential for different aproaches based on the available data quality.
We have more than 20 success stories from different industries on our web page. Here are some examples:
*Vitra https://www.sdil.de/en/projects/vitra-2 *EDI: https://www.sdil.de/en/projects/edi-2