This post was originally published on Info World
Computer vision is an increasingly important industrial technology, not only for managing product lines or stock control, but also for safety. It’s a powerful technology, able to quickly classify objects and identify anomalies. But there’s a problem that comes with using it at the edge of your network: latency. When people’s lives are on the line, you don’t want to rely on a mix of wired and wireless networks, or on cloud resources that may need to spin up before they can be used.
That’s one of the key reasons why Microsoft CEO Satya Nadella talks about “the intelligent edge,” i.e. bringing cloud native tools and services to devices running on our networks. We need to be able to access, if not everything, then certainly a subset of cloud services at the edge. And that most definitely means computer vision.
Microsoft has already provided tools to containerize elements of Azure Cognitive Services, including its custom vision tooling, and deliver them to its own Azure IoT Edge platform. But what if you’re rolling your own edge solution?
Machine learning containers at the edge
It turns out that containers are an ideal way to deploy software to the edge of the network. Kubernetes and service meshes
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