- Analytics & Modeling - Edge Analytics
- Platform as a Service (PaaS) - Edge Computing Platforms
- Discrete Manufacturing
- Process Manufacturing
- Edge Computing & Edge Intelligence
- System Integration
At Litmus, the biggest challenge the customers face is access to the data they need to fuel machine learning and analytics models. Large scale manufacturers come to Litmus looking for the fastest way to connect to their assets and send data to the cloud. Companies not only need to send data to the cloud to create machine learning models, but they also need to deploy those models back at the edge with a unified edge-to-cloud platform.
A unified edge-to-cloud data connectivity platform bridges the gap between industrial devices at the edge and big data and machine learning systems in the cloud. The platform is deployed on edge computing devices and is designed to collect and structure data into a common data format. A unified edge-to-cloud platform acts as a data broker, enabling applications and analytics at the edge, and big data and machine learning in the cloud.
Litmus is deployed as an edge computing platform next to the industrial assets – collecting and normalizing data at the source. Ready-to-use data is managed at the edge for local analytics and sent to cloud and big data systems for more complex processing. Data models are deployed back at the edge to complete the cycle.
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.