Network Local Edge Computing Architecture for Machine Learning

A network-local design pattern is similar to native-edge, but it can handle larger workloads. Here, you are deploying your model(s) to an enterprise-grade edge server with dedicated resources that can process large data workloads simultaneously. This design pattern works well for use cases where you have hundreds or even thousands of sensors/cameras collecting data that must be processed quickly. Not only does it support scaling to thousands of devices, by using a central MLOps platform, you gain governance, the ability to manage multiple model versions, and visibility into performance across all locations.

Examples: Air quality monitoring or PPE detection for worker safety, supply chain management, inventory prediction, quality assurance on an assembly line, computer vision- enabled perimeter security, etc.

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