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.
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
Let customer data stay with its owner through local machine learning
mlx enables you to coordinate on-the-edge training, deployment, and management of ML models easily on the customer’s shop floor. There is no need to transfer sensitive customer data.
Just quickly upgrade industrial PCs in the customer’s local environment with mlx using minimal effort.
Exchange local models in order to adapt to diverse machine domains
Industrial customers often vary through their local requirements. Machines are used in different settings or processes and need models tailored to their specifics.
Using decentralized ML techniques, mlx enables secure model exchange across machines and customers – without exchanging raw data.
Exchange local models in order to adapt to diverse machine domains
Industrial customers often vary through their local requirements. Machines are used in different settings or processes and need models tailored to their specifics.
Using decentralized ML techniques, mlx enables secure model exchange across machines and customers – without exchanging raw data.
Benefit from asynchronous communication
With mlx, your ML training is unaffected by machines being offline. Machines will be enabled to autonomously run ML on-the-edge and outbound communicate with your central node.
In comparison, other Decentralized Machine Learning solutions require machine and node to constantly be in sync.
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