As the demand for AI-based solutions continues to rise, there’s a growing need to build machine learning pipelines quickly without sacrificing quality or reliability. However, since data scientists, software engineers, and operations engineers use specialized tools specific to their fields, synchronizing their workflows to create optimized ML pipelines is challenging.
A powerful open-source container orchestration system, Kubernetes automates the deployment, scaling, and management of containerized applications. It’s a popular choice in the industry these days. Automating tasks like load balancing and rolling updates leads to faster deployments, improved fault tolerance, and better resource utilization, the hallmarks of a seamless and reliable software development lifecycle.
With the rise of cloud computing, containerization, and microservices architecture, developers are adopting new approaches to building and deploying applications that are more scalable and resilient. Microservices architecture, in particular, has gained significant popularity due to its ability to break down monolithic applications into smaller, independent services.
Kubernetes orchestrates the management of containerized applications, with an emphasis on declarative configuration. A DevOps engineer creates deployment files specifying how to spin up a Kubernetes cluster, which establishes a blueprint for how containers should handle the application workloads.
I am happy to share that thanks to the power of the open-source community, and our friends over at Otterize, we have now enhanced our Kubernetes offering for developers with another visual aid to streamline operations and troubleshooting – Dependencies Map. The Otterize network mapper is a zero-config tool that aims to be lightweight and doesn’t require you to adapt anything in your cluster.
A couple of weeks ago I had the absolute joy of attending KCD Munich for the first time, with my friend and colleague Guy Menahem (whom some of you know simply as The Good Guy on Twitter and YouTube). Besides rooting for Guy and his co-speaker, Arsh Sharma of Okteto, during their session on Backstage.io and IDPs, I enjoyed being untethered from ‘booth duty’ and free to engage with all the beautiful human beings that gathered together for this Kubetastic event!
Helm Dashboard is an open-source project which graphically shows installed Helm charts, revisions, and changes to their Kubernetes resources. The intents operator is an open-source Kubernetes operator which makes it possible to roll out network policies in a Kubernetes cluster, chart by chart, and gradually achieve zero trust or network segmentation.
We’re pleased to announce that the Komodor platform has published an Amazon Elastic Kubernetes Service (Amazon EKS) Blueprints CDK Add-On. Amazon EKS is a managed Kubernetes service that streamlines the deployment and scaling of cloud-based or on-prem K8s clusters.
In Part 1 of this series, you learned the core components of Kubernetes, an open-source container orchestrator for deploying and scaling applications in distributed environments. You also saw how to deploy a simple application to your cluster, then change its replica count to scale it up or down. In this article, you’ll get a deeper look at the networking and monitoring features available with Kubernetes.
Put simply, Kubernetes is an orchestration system for deploying and managing containers. Using Kubernetes, you can operate containers reliably across different environments by automating management tasks such as scaling containers across Nodes and restarting them when they stop. Kubernetes provides abstractions that let you think in terms of application components, such as Pods (containers), Services (network endpoints), and Jobs (one-off tasks).
