In this example, the flow is reading OPC-DA data and prepare it for a Python model running at the edge:
- Apply algorithms to extract features from the data, eg objects, patterns, trends…
- Correlate data from multiple sensors to derive higher-level insights
- Use compression techniques to reduce the amount of data without reducing the relevant information.
- Collect OPC data
- Pre-process data
- Time align
- Run Machine Learning model
Introduction to Machine Learning at the Edge
Free on-demand webinar
Intro to applying Machine Learning at the Edge. During this 30 min session, Crosser CTO, Goran Appelquist Ph.D. will introduce you to designing a functional IOT data flow for industrial usage within Crosser Flow Studio.
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- Sign up
- Login and start designing your data flows
- Run the flow in Crosser test environment or download local test node
- Test with real data