November 11, 2020 | by Nick Barcet
Many industries, including industrial/manufacturing, are bringing together the powerful combination of edge computing and AI/ML to transform their operations and fuel innovation faster by bringing processing power closer to data. In our previous blog post "Boosting manufacturing efficiency and product quality with AI/ML, edge computing and Kubernetes", we explained how and why someone would use OpenShift at the edge on a factory floor, we have now released version 1.0 as a complete GitOps repository which everyone can use, study and even contribute to. In the repository you will find:
We created the blueprint with a few key goals in mind:
For this particular solution blueprint, we demonstrate how OpenShift, ACM, AMQ Streams, OpenDataHub, and other Red Hat products come together to address an edge computing use case commonly found in manufacturing: Machine inference-based anomaly detection on metric time-series sensor data at the edge, with a central data lake and ML model retraining. You can watch a full demonstration of this which we recorded for an OpenShift Commons briefing.
We really hope to see you on our Git soon!
To learn more, visit www.openshift.com/edge
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