The latest News and Information on DevOps, CI/CD, Automation and related technologies.
As a leading, open-source multi-cluster orchestration platform, Rancher lets operations teams deploy, manage and secure enterprise Kubernetes. Rancher also gives users a set of container network interface (CNI) options to choose from, including open source Project Calico.
We’ve added production instances in two new locations: US-WEST-2 (Oregon) and AP-SOUTHEAST-2 (Sydney). These are just the first of many, but together they deliver serious performance improvements for our customers around the globe.
The linux-aws 4.15 based kernel, which is the default kernel in the Ubuntu 18.04 LTS AMIs, is moving to a rolling kernel model.
Red Hat OpenShift is a Kubernetes-based platform that helps enterprise users deploy and maintain containerized applications. Users can deploy OpenShift as a self-managed cluster or use a managed service, which are available from major cloud providers including AWS, Azure, and IBM Cloud. OpenShift provides a range of benefits over a self-hosted Kubernetes installation or a managed Kubernetes service (e.g., Amazon EKS, Google Kubernetes Engine, or Azure Kubernetes Service).
In Part 1, we explored three primary types of metrics for monitoring your Red Hat OpenShift environment: We also looked at how logs and events from both the control plane and your pods provide valuable insights into how your cluster is performing. In this post, we’ll look at how you can use Datadog to get end-to-end visibility into your entire OpenShift environment.
In Part 1 of this series, we looked at the key observability data you should track in order to monitor the health and performance of your Red Hat OpenShift environment. Broadly speaking, these include cluster state data, resource usage metrics, and information about cluster activity such as control plane metrics and cluster events. In this post, we’ll cover how to access this information using tools and services that come with a standard OpenShift installation.
Kubeflow v1.0 was released on March 2, 2020 Kubeflow and there was much rejoicing. The banner announcement, “Cloud-Native ML for Everyone,” while clearly hyperbole, is evidenced by the streamlined command-line interface (CLI), informative and intuitive dashboard and comprehensive cloud provider documentation. Compounded with a best-in-class product suite supporting each phase in the machine learning (ML) lifecycle, Kubeflow stands unrivaled in the arena of ML standardization.
