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In this example, the flow is reading OPC-DA data and prepare it for a Python model running at the edge:

  1. Apply algorithms to extract features from the data, eg objects, patterns, trends…
  2. Correlate data from multiple sensors to derive higher-level insights
  3. Use compression techniques to reduce the amount of data without reducing the relevant information.

Steps

  1. Collect OPC data
  2. Pre-process data
  3. Time align
  4. 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.

Level: Intermediate
Time: 00:30:31

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Start innovating today. How it works:

  1. Get an account
  2. Log in and start designing your flows in the sandbox
  3. Download the Crosser Container to your local test node
  4. Test with real data