Crosser and Weidmüller, a provider of advanced IIoT & Analytics Services, have announced a partnership to deliver an end-to-end Machine Learning solution that empowers subject matter experts to build and deploy machine learning powered use cases, without the support of data science experts.
According to the announcement, the joint solution is a full circle solution that combines the unique low-code capabilities of both companies to deliver a complete solution that anyone can master.
- Hosting of the Crosser Edge Node software on Weidmüller hardware. The growing family of IoT devices in the Weidmüller product portfolio will be able to use the Crosser Edge Node software through a simple one-click installation via the u-create web add-on manager.
- The low-code Crosser Edge Analytics platform allows users to collect and prepare industrial data, host and run trained models in the edge and take real-time actions with Intelligent Workflows.
- The Weidmüller Industrial AutoML solution empowers subject matter experts to train models using the no-code ModelBuilder where anomalies and normal state patterns are highlighted in a point-and-click manner.
The AutoML tool empowers customers to transfer their data and domain knowledge into ML models that generate value for their business. We believe the combined solution addresses all steps in the life cycle of ML powered use cases and will accelerate the pace of innovation for the customers.”
“It is when a ML model becomes actionable, the full value of the model is extracted,” Dr. Carlos Paiz Gatica, Product Owner for Industrial AutoML at Weidmüller explains. “The Crosser low-code solution takes care of everything around collecting and preparing data for training and execution of the ML models, including the important mass-orchestration of new or updated ML models to a large volume of Edge Nodes.”
Machine Learning is a game changer for the industry because it is a technology that makes it possible to arrange tasks such as monitoring of processes or machines in a way that the effort for OEMs or plant operators is significantly reduced. The combination of application knowhow and data science knowledge plays a major role in the development of the solution. And this is also the challenge for the industry. Finding employees with the right data science skills is not that easy. The combined solution enables plant operators and OEMs to use Machine Learning without the need for own data science experts.
Crosser | data management | edge AI | IIoT | inference | model training | Weidmüller