In this post we introduce the newly released EKS functionality in the Cluster API Provider for AWS (CAPA) and then walk you through the creation of your first EKS cluster. Finally we’ll cover the functionality you can expect to be added to future releases of CAPA.
In this article, we discuss Modern Operations, and what it looks like in an enterprise environment. We’ll also discuss the advantages of modernizing the technology stack and development practices in an enterprise. Finally, since everything good comes at a cost, we’re going to have a very candid and open conversation about the foundational elements you’ll need to put in place to ensure that your modernization strategy pays off.
Download this whitepaper to learn what cloud native technology looks like and what you need in order to make the shift to cloud native.
GitHub Actions is a beta feature that allows you to automate tasks and create workflows. Find out how to create GitOps pipelines with GitHub Actions and Weave Cloud.
Learn how we’ve teamed up with our security partner Twistlock to show you how you can increase reliability and velocity through implementing a GitOps model while keeping an eye on vulnerabilities and compliance through DevSecOps best practices.
How do you know when you are production ready? What does it even mean to be production ready? Download this whitepaper and find out what production ready means, the cultural changes you need to make on your team, as well as the most important requirements to consider when using and taking advantage of Kubernetes in production.
Managing distributed applications can be challenging. Find out how you can use Weave Cloud to manage, debug and monitor applications running in Istio and Kubernetes.
The OpenFaaS team recently released a Kubernetes operator for OpenFaaS. The OpenFaaS Operator can be run with OpenFaaS on any Kubernetes service, in this post I will show you step-by-step instructions on how to deploy to Amazon's managed Kubernetes service (EKS).
See how Qordoba doubled their productivity by using Weave Cloud for Continuous Delivery to deploy machine learning models to Kubernetes.
Watch this short architecture overview video to learn how Weaveworks monitors clusters at scale using a highly available, multi-tenant system built on AWS services.
Discover how to achieve an automated continuous delivery with Kubernetes, Google Cloud Platform and Weave Cloud multiple time a day. Discover the role, observability and metric monitoring play for determining whether you’re progressing once you’ve increased your speed of deployment.
This blog post explains techniques for development teams who strive for high velocity continuous delivery using Kubernetes and Docker. When we say “high velocity” we mean that every product team can safely ship updates many times a day — deploy instantly, observe the results in real time, and use this feedback to roll forward or back. The goal is for product teams to use continuous experimentation to improve the customer experience as fast as possible.
This step by step tutorial shows how to set up Kubeflow, a tool that simplifies set up of a portable Machine Learning stack and Weave Cloud on the Google Cloud Platform. Kubeflow users will then be able to use Weave Cloud to observe and monitor the stack, including metrics for resource management.
Sealed Secrets is a Kubernetes Custom Resource Definition Controller which allows you to store even sensitive information (aka secrets) in Git, which previously has not been an option. In addition, you can use Weave Cloud’s Deploy feature in conjunction with Sealed Secrets to create a continuous deployment pipeline where all operations are git based and where the desired state of your apps is declared in your git repos including your secrets.
In this post we will be discussing how to set up application and infrastructure monitoring for Docker Swarm with the help of Prometheus. Swarmprom is a starter kit for Docker Swarm monitoring with Prometheus, Grafana, cAdvisor, Node Exporter, Alert Manager, and Unsee.
Weave Cloud works alongside machine learning platforms such as Seldon’s. In this tutorial you will deploy a predictive service that recognizes drawn numbers from 0 to 9.