In today’s digital landscape, users expect applications to be available at all times and developers are expected to deploy new versions of these applications several times a day. Both of these expectations can be met by upgrading your Kubernetes cluster. Kubernetes is constantly getting new features and security updates, so your Kubernetes cluster needs to be kept up-to-date as well.
D2iQ Konvoy provides controls to easily upgrade Kubernetes itself, Kubernetes add-ons, like Prometheus, or the Konvoy CLI independently. Best of all, upgrades can be performed in place without disruption. This tutorial will cover how to upgrade node pools, or specify the upgrade based on specific node pools. We’ll also walk through how to upgrade in parallel, or specify the number of concurrent nodes to upgrade. To get started, take a look at the cluster config.
D2iQ Konvoy simplifies the deployment on Azure by providing a command line interface to automate the deployment and operations of Kubernetes clusters all in one place. In this tutorial, we’ll show you the provisioning of an enterprise-grade Kubernetes cluster on Azure using a single command. Before we get started, let’s talk about a few prerequisites you’ll need: First, download the D2iQ Konvoy installer and authenticate it to your Azure account.
If your organization is planning to use AWS for deploying new releases, the deployment process can be tricky for teams outside of operations to learn and use, especially for those who don’t have expertise in the tooling to automate deployment. And, because there are several ways to deploy Kubernetes on AWS, including Amazon’s own EKS, understanding the different deployment options can be tough to navigate.
Today’s enterprise organizations are using some form of multi-cloud infrastructure, and the numbers don’t lie. According to Flexera’s 2020 State of the Cloud report, an average of 2.2 public clouds are being used per enterprise company. And in a different report from the Everest Group, 58% of enterprise workloads are on hybrid or private cloud. The sheer increase in multi-cloud usage illustrates it’s growing popularity across enterprises.
AI and Machine Learning (ML) are key priorities for enterprises, with a recent survey showing that 72% of CIOs expect to be heavy or moderate users of the technology. Unfortunately, other research has found that the vast majority—87%—of AI projects never make it into production. And even those that do often take 90 days or more to get there. Why this disconnect between intent and outcome? What are the roadblocks to enterprise ML? And what can be done about them?
