Canonical
on 4 March 2019
How to quickly deploy production-ready Kubernetes
Kubernetes, the flexible software that coordinates containers, runs on a range of platforms, from public to private cloud, data centers, bare metal and virtualised infrastructure, and requires a number of considerations for design and performance optimisation. Beyond identifying individual requirements, such as use cases and topology, organisations must also determine needs for compute, storage, networking and GPUs. While the enablement of containers brings clear benefits in creating efficiency in workflows and workloads, infrastructure planning for conversion into a microservices environment can seem a daunting task.
<Moving to Production-Ready, Fast, Affordable Kubernetes>
It doesn’t have to be, though. Supermicro and Canonical together provide a complete solution that leverages pre-validated design and integrations for an efficient, scalable, application-centric cloud solution that can be deployed in days rather than months. This all-in-one cluster is based on Supermicro hardware and Canonical Charmed Kubernetes containers and is built for performance, capacity and cost, while facilitating use cases such as AI/ML.
Beyond deployment, organisations must consider the process to handle upgrades on and management of their applications. Every cloud has unique features and a DevOps system needs to be orchestrated in a way that makes room for delivery and innovation. Canonical’s Charmed Distribution of Kubernetes is pure upstream, with guaranteed upgrades, security updates and runs across multiple infrastructures, while being cost effective at scale.
Kubernetes has become a core component in delivering cloud-native applications quickly and efficiently. If you want to find out more, please register for our webinar, Moving to Production-Ready, Fast, Affordable Kubernetes, taking place on 14 March to learn about:
- How to optimise design considerations and performance
- Integrations for compute, GPUs, storage and networking
- How to deploy a kubernetes cluster in days rather than months
- Moving teams that are running containers over the hump into production grade, and available support
- Managing production grade container deployment
- Support for AI/ML workloads – build, train, evaluate and deploy machine learning models
