Our experienced ML researchers and engineers develop a proof of concept in a joint project. The PoC demonstrates how your data can be optimally processed with ML algorithms. Of course, your data and code will be treated with maximum protection in the process.
Ideally, machine learning can create magic in the eye of the beholder. In practice, however, many things must be developed with the available data in mind. Appropriate solutions are best developed iteratively between subject matter experts and machine learning experts to maximize the value of a data set using the latest methods. The resulting proof of concepts are often the basis for successful business development.
Their goal is to attract investors and inform development decisions. The joint development of a PoC with you is performed by experienced ML researchers from 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 also focuses on innovation and technology transfer, our experts have many years of experience from applied industrial projects. Our main focus is on applications in industry, healthcare, and finance that involve machine learning on time series data. We also have a strong background in environmental monitoring with sensor networks and IoT, as well as mobile and wearable computing.
Specifically, we can provide hosted machine learning and web and database services as part of PoC development. The software will be developed in an agile manner. The necessary project management support and infrastructure can be provided upon request.
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.)
If you decide to propose the participation of KIT and SDIL infrastructure in your experiment, you must respect the following conditions. 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.
Clear specification of the machine learning objective, fitting labeled data set(s).
Once you can define a supervised machine learning problem and have identified fitting labeled data sets, you may want to demonstrate the feasibility of this concept in a prototype. Our experts help you to build this prototype based on their experience from reseach and industry projects.
Recently we have build a PoC service for embedded machine learning to promote a sensor platform produced by one of our industry partners: https://edge-ml.org/