I’m pleased to announce that Mariner has selected Microsoft’s Azure Stack Edge as our preferred edge intelligence platform. Edge intelligence is an essential component for manufacturers deploying digital transformation or Industry 4.0 technologies. Edge intelligence plays a vital role in a performant, secure, and available architecture for our Spyglass Visual Inspection product.
The primary benefits of the Edge Intelligence server is to:
- Reduce latency. For example, Spyglass Visual Inspection depends upon a vision model trained to detect defects in products. It does this in real-time by capturing images from cameras of products at various stages of production then asking the vision model to make a call: pass or fail the quality inspection test. This pass/fail result is then passed back to the production line for proper disposition of the product. Usually, these decisions must be made in a very short period of time. If the industrial vision system depended upon access to the model in the cloud, the round trip could be an unacceptable time loss. An edge intelligence server provides these services at the factory eliminating the latency of the Internet.
- Permit continued operations when the Internet is down. Many of our customers have factories in far flung areas with a ready workforce. In some of these factories, the internet connection has not been considered a high priority. Should the Internet go down, the Edge Server is capable of continuing operations for a very long while. When connectivity is restored, the Edge Server synchronizes with the cloud as needed without loss of service.
- Reduce cloud compute and storage costs. The Edge Server can capture messages and telemetry as frequently as it is generated. Since cloud traffic is often priced per message and storage is priced per transaction, the Edge Server can summarize messages to a unit of time that still provides the benefits of getting telemetry with the unit of time needed to provide effective time series analysis. For example, if you have a device generating messages every tenth of a second, the Edge Server can respond to emergent conditions in real-time. It can also provide summarize these transactions and distill them down to min, max, & mean of values in a minute’s worth of data for submission to history significantly reducing the cloud services and storage required without loss of fidelity.
- Enterprise Scalability. Edge servers are an integral component to permit enterprise scalability. The edge server is where the AI models are stored and executed. But AI models are not static and need to be refreshed for reasons of model drift, new products or changing production configurations. An organization’s AI models can be stored in the cloud and “pushed” down as updated models are made available.
- Spyglass Visual Inspection applies the advantages of a hybrid/cloud deployment to centralize model management in the cloud and simultaneously provide local control of real-time operations at the edge.
In the cloud:
- You maintain your labeled image libraries. The centralized store allows you to train on images gathered from any site and line, so pooling the examples of rare defects and improving the model’s ability to recognize them.
- Label new images that are flagged for investigation adding them to the collection.
- Re-train and evaluate models without burdening the edge workloads.
- Maintain histories of model versions, their over-the-air deployments and on-site performance.
At the edge:
- You only make a small percentage of defects so the edge only send copies of interesting images to add to the cloud collection, thus conserving bandwidth and balancing out libraries.
- Receive new models over-the-air, but only introduce them using policies specific to each edge device. The process owners decide when and how to introduce new models with partial cut-overs to prove efficacy and so they always know what versions are being used and choose when they are introduced.
- Receive over-the-air updates to software that will only be installed on an approved outage. Process owners are in full-control of the upgrades to Spyglass Visual Inspection.
I hope this primer on edge intelligence has been helpful. If you’d like a deeper dive, here is a video of Microsoft’s David Armour, Principal PM Manager, Azure Stack and Mariner’s Peter Darragh, VP of Product Engineering doing a deep dive on Microsoft’s Azure Stack Edge.
