This post introduces a new feature called Profiles, which allows you to create a specific Kubernetes application platform to meet your business needs. We show how you can enable machine learning operations or MLOps with specific Profiles for two different types of Kubernetes instances - EKS and Kubernetes with Firekube.
Flagger and Weave Cloud now supports the Service Mesh Interface (SMI) API for advanced deployments like canary to Kubernetes. SMI is an open project started in partnership with Microsoft, Buoyant, HashiCorp, Solo, F5, Red Hat, and WeaveWorks.
Read about our new Weave Cloud feature that lets you promote workloads between clusters. Find out how this can help your team accelerate the delivery of features to Kubernetes.
Catch up on the latest feature release for Weave Cloud that focuses on understanding deployments. Use batch mode to make multiple workload deployments or continue using single mode and select individual workloads to deploy to the cluster. With greater control over your deployments, you can more easily manage when and how new workloads land on either your staging or production servers.
Catch up on the latest feature release for Weave Cloud that focuses on understanding workloads. We'll talk about workload views, as well as cluster-wide workload views. At a glance you can locate any service and determine what was deployed when and by whom.
Part 4 of the GitOps blog series explains how you can make your continuous delivery pipeline more secure using 3 best practices which enables auditing and compliance. This is aimed at Kubernetes users who have adopted Continuous Integration (CI) and who want to add Continuous Deployment (CD) to their pipeline.
While Helm helps with getting applications running, you need other tooling to keep them up to date when something changes. Check out our alpha release of our Helm integration in Weave Flux. It makes sure your Helm chart releases are kept up to date with what’s in Git. You can try it out using the published repository in Github.
Weave Cloud Deploy is a Continuous Deployment/Delivery operator for Kubernetes which makes it super easy to automate releases to a cluster - but what about multiple clusters? Weave Cloud Deploy can filter tags - by tagging each image with an environment specific prefix, it’s possible for the same image build to be automatically released to each environment in turn.
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.
This post shows step by step how to set up a multi-cloud environment for big data processing using Apache Flink, Docker Swarm and the new Weave Net Docker plugin.
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.
We've partnered with the Google team to create a simple set-up process that makes it easy to experience the power of Kubernetes (GKE) with Weave Cloud’s management capabilities. If you're exploring Kubernetes and want a completely integrated CI/CD pipeline with observability and monitoring then this gives you everything you need.
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 tutorial you will learn how to set-up OpenFaaS on Kubernetes with monitoring and alerting using Weave Cloud. Using OpenFaaS for serverless avoids being locked-in to one of the cloud vendors. It's easy to run in Kubernetes, letting you mix different services depending on your applications needs.
This practical guide will help you getting started with monitoring your microservices with Prometheus. We walk through selecting key metrics, instrumentation, setting up alerts and Grafana dashboards.