Applying Machine Learning to Industrial Machinery Is Challenging for Providers
Restricted data access
Customers are not willing to share their data. This may be due to legal, strategic or technical restrictions.
Diverse requirements
Machines, processes and customers are different, making it hard to apply one-size-fits-all ML models.
Availability of local IT systems
The availability of local IT systems varies, because they are busy or intermittently have no internet access.
Restricted data access
Customers are not willing to share their data. This may be due to legal, strategic or technical restrictions.
Diverse requirements
Machines, processes and customers are different, making it hard to apply one-size-fits-all ML models.
Availability of local IT systems
The availability of local IT systems varies, because they are busy or intermittently have no internet access.
60% of German mechanical engineering companies want to roll out ML-based solutions, like predictive maintenance or machine vision, across customers by 2022.
VDMA, 2019
53% of German companies struggle with the application of machine learning because of strict data exchange standards.
IPSOS & iCite, 2020
Decentralized Machine Learning with mlx
Model exchange instead of data exchange
Data stays with its owner
mlx enables you to coordinate training, deployment, and management of ML models easily on the customer’s shop floor. There is no need to transfer sensitive customer data. Quickly upgrade industrial PCs in the local environments with minimal effort using mlx.
Adapt to diverse customer requirements
Industrial customers often vary through their local requirements: Machines are used in different settings or processes. With mlx, models can be tailored to these specifics.
Asynchronous communication
With mlx, your ML-based feature or service is unaffected by machines being temporarily offline. ML Models are deployed on-edge and will run autonomously. Further, machines have an outbound communication and connect with the cloud when they are back online.
Develop the models in the local environment – without the need to centralize data.
Don't just take our word for it
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