Node-RED Support for Effortless Automation

Our Scailable middleware, the Scailable AI manager, makes AI/ML model deployment effortless. Our value is to abstract away from specific hardware targets (i.e., if you are using Scailable to deploy to any supported device, as a developer you don’t have to worry about the target or the accelerators therein) and allow virtually any modeling platform to be used to create your AI models (i.e., TensorFlow, PyTorch, or EdgeImpulse). Scailable’s managed deployment of the full pipeline on your targeted device ensures that developers of new edge AI solutions can move and scale fast, not worry about deployment targets, and iterate rapidly and cheaply.

All of that should be sufficient to try out our platform for yourself, but in many applications “merely” seamlessly merging the data science development cycle and the embedded engineering development cycles is not all: in virtually any successful edge AI solution the AI model inferences are used in some higher level application. This is why we seamlessly integrate with Network Optix (Nx): any visual application is supported out-of-the box by their amazing video management system. And, witch Scailable directly integrated, any AI model, running on any Nx camera stream, can be directly visualized. Pretty cool.

However, edge inference not always feed into visualizations, higher level dashboards, or alarms. In many cases, especially in industry, edge inferences are used to power further automations. We might want to direct a COBOT, or trigger an actuator. Or, we might want to send data to co-located PLCs. In comes NodeRed: “Node-RED is a programming tool for wiring together hardware devices, APIs and online services in new and interesting ways. It provides a browser-based editor that makes it easy to wire together flows using the wide range of nodes in the palette that can be deployed to its runtime in a single-click.” So, we obviously integrated with NodeRed.

We have just written up how to forward data from the AI manager to NodeRed and setup the flow start to end. Want to give it a try? Follow our newly released docs section: Or, if you are interested in seeing things unfold; check out the video below.


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