When it comes to best practices with Kubernetes, whether it’s at the edge or not — but especially at the edge, considerations for organizations that are tackling this, there’s four major areas of consideration.
Transcript for Jason Zeng, Senior Director and Practice Lead - Solution Architects, on Kubernetes at the edge:
The first is, we think of Kubernetes as a common application management platform, being the front runner, being a platform that everyone in the world is basically, constantly trying to add features to, add capabilities and iterate on, you're kind of staying in the forefront of that technology.
The second benefit to Kubernetes at the edges, there's a lot of development for Kubernetes when it comes to integrating service meshes and also with manageability — and also, adding that layer of manageability removes a lot of risks and removes a lot of constraints for organizations that don't want to just build and manage this completely from within.
A third benefit is multi-cloud. For customers that are deploying Kubernetes, that’s one of the key benefits. You don't want to be tied to one specific public cloud vendor.
The fourth consideration would be, if your organization is doing a lot of hands-on data science, machine learning, and you're wanting to optimize machine learning operations — you're doing various interesting workloads, such as inferencing and training workloads related to things like computer vision — Kubernetes is somewhat of an important aspect in your overarching data science life cycle.
The reason we say that is because, in order to deploy machine learning in an optimal fashion, in a way that is clean, you have to have a very optimized and clean container environment and free to get there, Kubernetes is one way to do that.
We have a very strong hardware deployment background, and we’re bringing that along with our OEM appliance background, [and] we’re merging with our Kubernetes practice together. And, that provides a tremendous amount of value for our customers.
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