Running machine learning (ML) models at the edge can be a powerful enhancement for Internet of Things (IoT) solutions that must perform inference without a constant connection back to the cloud. Although there are numerous ways to train ML models for countless applications, effectively optimizing and deploying these models for IoT devices can present many obstacles.
Some popular questions include: How can ML models be packaged for deployment across a fleet of devices? How can an ML model be optimized for specific edge device hardware? How can we efficiently get inference feedback back to the cloud? What ML libraries do …
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