Understanding Component Upgrades in an Upstream Rafay MKS Cluster

Upgrading a Kubernetes cluster is a crucial process that ensures your infrastructure stays up-to-date with the latest features, bug fixes, and security patches. As part of this process, several components within the cluster undergo upgrades.

In this blog post, we will explore the components that typically get upgraded during a cluster upgrade and highlight some of the periodic upgrades that both Cloud Service Providers (CSPs) and Rafay undertakes to enhance cluster performance and stability.

Developer Self Service via Cluster Templates

Our recent release update in May adds support for a number of new features and enhancements and we have written about the these enhancements and new features in our blogs. This blog is focused on Cluster Templates for GKE that enables customers to implement a Developer Self Service for Kubernetes clusters.

We added support for cluster templates in early 2022 starting with support for Amazon EKS initially, then followed by cluster templates for Azure AKS and with this release, cluster templates for Google's GKE. Common Use Cases for Cluster Templates are "Ephemeral Clusters" for lower environments such as:

  • Developer Test Beds
  • QA environments
  • Product support to replicate customer issues

Amazon EKS v1.25 using Rafay

Our recent release update in May to our Preview environment adds support for a number of new features and enhancements. We will write about the other new features in separate blogs. This blog is focused on our turnkey support for Amazon EKS v1.25.

Both new cluster provisioning and in-place upgrades of existing EKS clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that every feature in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version.

This release will be promoted from Preview to Production in a few days and will be made available to all customers.

Note that no action is needed on the part of our SaaS customers with the new release. Once the rollout is completed, all they need to do is learn about the new features and determine how and when they would like to use them.

Kubernetes v1.26 for Rafay MKS

Our recent release update in May to our Preview environment adds support for a number of new features and enhancements. We will write about these in separate blogs. This blog is focused on support for Kubernetes v1.26 with Rafay MKS (i.e. upstream Kubernetes for bare metal and VM based environments).

Both new cluster provisioning and in-place upgrades of existing clusters are supported. As with most Kubernetes releases, this version also deprecates and removes a number of features. To ensure there is zero impact to our customers, we have made sure that every feature in the Rafay Kubernetes Operations Platform has been validated on this Kubernetes version. This will be promoted from Preview to Production in a few days and will be made available to all customers.

Kubernetes v1.26 Release

AI/ML Superpowers for Kubernetes Troubleshooting

In the last two blogs (part 1 and part 2), we discussed the challenges customers face with running AI/ML on Kubernetes and innovative solutions to address these challenges. In this blog, we will flip this on its head and look at how AI/ML can make Kubernetes easier to use and operate.

Solutions for Key Kubernetes Challenges for AI/ML in the Enterprise - Part 2

This is part-2 of our blog series on challenges and solutions for AI/ML in the enterprise. This blog is based on our learnings over the last two years as we worked very closely with our customers that make extensive use of Kubernetes for AI/ML use cases. In part-1, we looked at the following:

  • Why Kubernetes is particularly compelling for AI/ML.
  • Described some of the key challenges that organizations will encounter with AI/ML and Kubernetes

In this part, we will look at some innovative approaches by which organizations can address these challenges.

EKS Anywhere Bare Metal Cluster Management Made Easy with Rafay

Amazon Elastic Kubernetes Service (EKS) is a managed service that you can use to run Kubernetes on AWS without needing to install, operate, and maintain your own Kubernetes control plane or nodes. One of the options available with EKS is EKS Anywhere for Bare Metal environments, which allows you to run Kubernetes on your own hardware. While this provides more control and flexibility to businesses, it also comes with its own set of challenges.

While the benefits of managing EKS Anywhere on Bare Metal are significant, it’s important to note that the process can be challenging and time consuming, particularly if you lack experience with kubernetes on Bare Metal infrastructure. This is where Rafay’s Kubernetes Platform for managing EKS Anywhere Bare Metal clusters can come in handy.

In this blog, we'll explore how Rafay’s Platform can address pain points and simplify the management process.

Rafay's Terraform Provider

Terraform is today one of the most popular tools to provision resources on all major cloud platforms, such as Amazon Web Services, Google Cloud Platform, and Microsoft Azure. It uses Infrastructure as a Code (IaC) to automate infrastructure provisioning. This blog will discuss the Terraform provider for Rafay.

The Rafay Terraform provider Terraform Provider is a plugin that allows Terraform to manage resources in the Rafay platform. It enables users to automate the creation, configuration, and deletion of Rafay resources such as clusters, projects, policies, and environments. It is available as an open-source project on GitHub, and it can be installed using the standard Terraform plugin installation process Terraform installation

Key Kubernetes Challenges for AI/ML in the Enterprise - Part 1

This blog is based on our learnings over the last two years as we worked very closely with our customers that make extensive use of Kubernetes for AI/ML.

This is part-1 of a two part series. In part-1, we will

  • Start by looking at why Kubernetes is particularly compelling for AI/ML.
  • Describe some of the key challenges that organizations will encounter with AI/ML and Kubernetes

In part-2, we will look at ways by which organizations can address these challenges.

Announcing our April 2023 (v1.24) Release

A few weeks back in early April 2023, we upgraded our Preview environment to v1.24 of the Rafay Kubernetes Operations Platform. Our sincere thanks to our customers and partners that have been actively testing the new functionality. We have received timely feedback that we have been able to incorporate into our product documentation and into the platform as well.

Today, we upgraded our Production environment to this release. As always, our customers will have seamless access to the new functionality with no interruptions to their applications or clusters. In this blog, I will describe some of the new features that are part of this release.

April Release v1.24