Deploying and managing machine learning (ML) models at the edge requires a different set of tools and skillsets as compared to the cloud. This is primarily due to the hardware, software, and networking restrictions at the edge sites. This makes deploying and managing these models more complex. An increasing number of applications, such as industrial automation, autonomous vehicles, and automated checkouts, require ML models that run on devices at the edge so predictions can be made in real time when new data is available.
Another common challenge you may face when dealing with computing applications at the edge is how to efficiently manage the fleet of devices at scale. This includes installing applications, deploying application updates, deploying new configurations, monitoring device performance, troubleshooting devices, authenticating and authorizing devices, and securing the data transmission. These are foundational features for any edge application, but creating the infrastructure needed to achieve a secure

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