KubeCon San Diego 2019 was a blast; lot’s of sun, beer, food, amazing projects and learning opportunities! It was great to see the community come together for the love of all things Kubernetes.
The Canonical booth was buzzing with excitement around MicroK8s, Multipass and Kubernetes clustering on that DIY Raspberry Pi clusters running Ubuntu and Kubeflow!
KubeCon was abuzz with exciting K8s projects and attracting a crowd of ~15,000 to sunny San Diego. The main themes and talks across KubeCon were Kubernetes(duh!), Kubeflow, and security. There were some pretty cool science projects, great networking and puppies!!
Canonical at Kubecon
The Canonical team was busy at the booth showcasing our multi-cloud Charmed Kubernetes and the exciting features of MicroK8s. Clustering and Kubeflow on Raspberry Pis, platform-agnostic Kubernetes deployments and full enterprise Kubernetes in a micro package.
It was a joy to see people’s reaction hearing about our MicroK8s and Kubeflow products. Single-line install of Kubernetes with Kubeflow on a Raspberry Pi in seconds; meet MicroK8s! We loved distributing an army of stickers, tees and beer to the wonderful attendees.
Until next time
Barely holding onto our voice, we hope to see you all in Amsterdam 2020. Until then, check out the Kubernetes that KubeCon at Canonical was fascinated by.
Canonical’s mission is to enable innovation and the open-source community, our projects can be found on Github. If you find bugs or would like to contribute, please open an issue on Github. Here’s a link to video recordings of the talks at KubeCon in case you missed it, happy learning!
With Charmed Kubeflow, deployment and operations of Kubeflow are easy for any scenario.
Charmed Kubeflow is a collection of Python operators that define integration of the apps inside Kubeflow, like katib or pipelines-ui.
Use Kubeflow on-prem, desktop, edge, public cloud and multi-cloud.
Kubeflow makes deployments of Machine Learning workflows on Kubernetes simple, portable and scalable.
Kubeflow is the machine learning toolkit for Kubernetes. It extends Kubernetes ability to run independent and configurable steps, with machine learning specific frameworks and libraries.
The Kubeflow project is dedicated to making deployments of machine learning workflows on Kubernetes simple,
portable and scalable.
You can install Kubeflow on your workstation, local server or public cloud VM. It is easy to install with MicroK8s on any of these environments and can be scaled to high-availability.