Weidmüller wins Industry 4.0 Innovation Award

Volker Bibelhausen (left) and Tobias Gaukstern (right) thankfully accepted the award at the fair smart production solutions from editor-in-chief Ronald Heinze (m.).

Automated Machine Learning Tool awarded prestigious prize

Detmold / Nuremberg, 27 November 2019. Electrical engineering company Weidmüller has been presented with the Industry 4.0 Innovation Award for its Automated Machine Learning Tool. Weidmüller Chief Technology Officer, Volker Bibelhausen, and Tobias Gaukstern, Head of Business Unit Industrial Analytics at Weidmüller, accepted the prize on 26 November at the Detmold-based company's stand at the Smart Production Solutions trade show in Nuremberg. “We are delighted that the jury and the community were won over by our innovative software solution and that we won this award by such a large margin,” explains Bibelhausen. “At this point, I would like to thank the entire Analytics team and of course all the people who voted for our software.” The Industry 4.0 Innovation Award was advertised and awarded in 2019 for the fourth time by VDE VERLAG GmbH in collaboration with ZVEI and the Standardisation Council Industry 4.0.

To be accepted to compete in the award, the products and innovations must make both a profit-generating and a supporting contribution in connection with Industry 4.0. “We are extremely delighted that Weidmüller won our Industry 4.0 Innovation Award in 2019 for its Automated Machine Learning Tool,” explains Ronald Heinze, Chief Editor of Open Automation and Head of Publishing of magazines. “Our publication wishes Weidmüller continued success with its award-winning solution to enable as many engineers and technicians as possible to use AI and ML-based models.”

With its Automated Machine Learning Tool, Weidmüller offers a software that domain experts can use to independently create ML models – without any special knowledge of Machine Learning (ML). “It is our vision to democratize the use of AI and ML in the industry with the Automated Machine Learning Tool. The vote confirms the need for easy to use ML solutions and gives us additional impetus for the further development,” adds Gaukstern.

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