How to Scale AI at the Edge

Given the promise of containers,

with software.

Two questions really here.

Well, it's more the promises to the new model, as you pointed out, but two questions here.

One is, presumably, it makes scaling to hundreds, thousands, potentially millions of devices so much easier because it's more consistent.

So I assume one of the benefits is Scaling deployment, security patching, making sure you've got consistent compliance, device lifecycle management.

Are they the sorts of things that are benefits? But they really become benefits because when we're talking IoT, we're talking millions.

potentially in the future, billions of devices.

So is that one of the reasons why these two technologies, IoT as a use case at the edge and containers for the software at the edge are natural bedfellows, so to speak, because of the fact that you can set a framework for all of those things and then do mass deployment scaling?

Yeah, when you learn the inner workings of a software container and how an image is constructed, like an image is basically just a zip of files and a bunch of configs on top of it, right? And so the software creator, so the vendor or an internal developer, creates the software package, creates an image, which bundles it up into an image file, which is then hosted centrally somewhere, and then devices out in the field, when they are commanded to go and run the container, they seek the image, pull the image, and then extract it and run it.

Every single device in the field, hundreds, thousands, millions of them, all refer back to the one base image.

They're all running that exact image.

Now, what's really cool is the software vendor says, oh my goodness, there's a bug, I'm going to push a new version.

They push a new version of the image.

Assuming these devices have the right control software, they will then detect there's a new version of the image and then immediately update themselves over the year by pulling the updates or the changes to the image and then deploy and go.

So that serves two benefits.

It helps you fix bugs with security and software defects.

That in a data center is useful and interesting.

Because you might have 100 or 1,000 servers, and let's say you're a large enterprise and you've got 50,000 servers.

Well, 50,000 devices is a pretty small IoT project.

The benefits of this mechanism in a data center is X, but the benefits of the same technology deployed at the far edge is probably 100 times X.

So yes, absolutely.

That's what I think has rapidly accelerated the use of containers at the far edge.

It's a good example of

actually why we renamed this podcast around both IoT and AI coming together, because deploying software to a series of servers in your branches or campus...