A fast and secure data flow connecting factories and enterprise cloud systems!
This document explains how customers can use 'Litmus' to achieve data connectivity at the edge and feed normalized, ready-to-use data and machine learning models. It also describes how to implement a feedback loop by operating new models on the asset side, enabling continuous optimization. Edge computing not only addresses challenges such as rising latency and costs to complement the cloud, but also allows for the operation of key use cases and applications that should ideally be hosted in on-premises environments. For more details, please feel free to contact us. [Reasons for the need for edge response] ■ Rapid insight acquisition ■ Immediate action ■ Bandwidth assurance ■ Use cases in on-premises environments *For more information, please refer to the PDF document or feel free to reach out to us.
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basic information
**How the Edge-to-Cloud Platform Works** - Collecting and normalizing data, then storing it in an immediately usable format - Data access through edge applications and analytical functions - Integration of edge data with cloud systems - Building and executing data models using edge data - Deployment and execution of data models on the edge - Collecting and analyzing data using new models *For more details, please refer to the PDF document or feel free to contact us.*
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Applications/Examples of results
【Usage】 ■ Data visualization ■ Machine learning and AI ■ Edge analytics ■ Continuous optimization *For more details, please refer to the PDF document or feel free to contact us.
Company information
About Litmus With Litmus, you can immediately start collecting and managing data analysis through our intelligent edge computing platform for IIoT. Litmus also provides solutions that transform critical edge data into actionable intelligence, enabling predictive maintenance, machine learning, and even AI. Our customers include more than 10 manufacturing companies ranked in the Fortune 500. Additionally, we are expanding our market reach in collaboration with partners such as Siemens, HPE, Intel, and SNC Lavalin.
