Edge Cloud Machine Learning Architecture

An edge cloud architecture is great for scenarios with variable model workload, so rather than deploying ML models to an edge server, you might want to deploy the entire platform to your facility. By giving your facility access to your entire library of models, it becomes easier to switch models in and out of applications. Additionally, this design pattern gives you the flexibility to send models directly to small sensors, which could be helpful for data science teams developing models tied to specific data that might not be relevant for other use cases. Here, you have the ability to create a private edge cloud, where models, data, infrastructure and sensors remain confined to this private instance. The main benefit of this architecture is access to the cloud and a central hub, with the ability to make models available locally.

Example: Any scenario where you might want to deploy a number of ML models to a location with sufficient compute capacity, but not guaranteed network connectivity. This could be on an oil rig, a remote facility, an AUV, or anywhere with inconsistent or spotty network connectivity.

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