KubeCon + CloudnativeCon Europe 2018
Tags: AI , AI/ML , Cloud Native , Kubeflow , kubernetes , machine learning , MLOps
Date: May 2-4
Location: Copenhagen, Denmark
Venue: Bella Center Copenhagen
The Cloud Native Computing Foundation’s flagship conference will be taking place in Copenhagen from May 2-4. It will cover Kubernetes, Prometheus OpenTracing, Fluentd, Linkerd, gRPC, CoreDNS, and other key technologies in cloud native computing.
The Ubuntu team will be on-site at booth G-C05. Here are some of the activities to look forward to!
- How Kubernetes can be used for scalable GPU computation from public cloud to private cloud
- How using Kubeflow to provision Machine Learning (ML) frameworks on top of Kubernetes makes it is easy to bring web scale to ML workloads
- How CDK’s out of the box NVIDIA acceleration makes deploying scalable GPU-accelerated workloads becomes a breeze.
Thursday May 3, 2018 11:55 – 12:30 in C1-M5: Write Once, Train & Predict Everywhere: Portable ML Stack with Kubeflow with Stephan Fabel (Canonical) and Jeremy Lewi (Google)
In this talk, we discuss how Kubeflow enables machine learning workflows that are easy enough for anyone to deploy, and run anywhere Kubernetes runs. We will talk about our experience building Kubeflow by leveraging Kubernetes technologies like CRDs and ksonnet to build an extensible, community driven ecosystem. Finally, we will talk about how we are trying to grow the community around Kubeflow to continue evolving the platform.
Want to speak to our team during the event? Contact us here!
Run Kubeflow anywhere, easily
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.
What is Kubeflow?
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.