Report: AI and IoT at the edge – when to move intelligence closer to the action
For industry, many critical applications cannot wait for artificial intelligence (AI) in the cloud. Instead, they demand insights and decisions at the edge — closer to the source, where the real action is. A new report by Enterprise IoT Insights considers the balance enterprises are striking between training machine learning algorithms in the cloud and executing them at the edge.
In many cases, these edge applications tackle the most interesting and valuable challenges for industry, which are often time-critical, business-critical, and / or bandwidth-intensive. The report, entitled AI and IoT at the cutting edge – when to move intelligence closer to the action, considers these industrial IoT use cases, and examines how enterprises can design an edge architecture to support them.
The new report from Enterprise IoT Insights is available to download for free.
James Blackman, editor of Enterprise IoT Insights, commented: “Training is a fitful labour, which should be carried out at a distance; inference is a constant task, which must be organised closer at hand. A hybrid architecture combines the muscle of cloud systems with the flexibility of edge systems.
“Beyond these disciplines, of heavy-duty analytics and light-touch inference, data collection should be maintained between the edge and cloud on a cycle of continuous improvement. This is the model that has been established for industrial IoT, and will underpin success in smart manufacturing.”
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