Our vision is that every automation expert and service technician should be able to generate and use Machine Learning models. A task that usually requires the specific know-how of a data scientist.
Our Automated Machine Learning tool allows you to generate and use models completely on your own. You will be able to recognise erroneous behaviour of your machines based on your own data and application knowhow. Our platform independent software helps you to do this with automated model generation and a simple and intuitive user interface. You decide whether you want to deploy your models on premise or in the cloud.
Benefit from additional service revenue stream attached to the sale of the equipment or from higher production and maintenance efficiency through the automation of monitoring processes.
Leverage your own domain knowledge to create optimized ML models
Empower your experts to build and deploy ML solutions without a data scientist or any additional trainings
From model generation, cloud or on-premise deployment, up to model optimization
The Automated Machine Learning Tool helped me to create my own analytics models in a short time without having any data science know-how. I was positively surprised about the good results the tool produced based on my application knowledge from the compressor. The model creation process and model selection was intuitive and easy for me to follow.
Dr. Markus Köster explains our Automated Machine Learning software that allows you to use AI- and ML based models independently, without the specific know-how of a data scientist.
We were fascinated by the solution, as we have a lot of process engineers who are very familiar with the machines and who are, to a certain extent, able to interpret the data. With Weidmüller's help, we can now transfer this knowledge to an algorithm
Learn more about the data science background of the Automated Machine Learning tool by our Data Scientist Dr. Daniel Kress
The tool will help you to easily optimize and enrich your deployed models by using additional data sets, thus continously increasing your model performance.