Scalability with automation
We often hear the term automation at scale, which really means using data analytics and ML tools to help with automation across an organization. From an edge computing perspective, it would translate to having automation as we move from right to left—from the cloud to the network to the enterprise all the way to the far edge devices.
Now imagine, if that trivial program shown previously was to be deployed on hundreds of cameras in a large automotive manufacturing plant, the IT department would use a configuration and management software tool to do that. The biggest benefit of automation is being able to scale and, very importantly, roll back if every device is not updated. No matter the number of devices, one or more DevOps pipelines would help with that task. Once tested, the same deployment task can be repeated many times without the concern of introducing any human errors.