After the successful launch and positive reception of the NVIDIA Tesla GPUs for Google Kubernetes Engine, the folks at Google Cloud Platform have introduced GPUs in Kubernetes Engine for a wider audience. We take a look at what this leap forward means for machine learning.
Serverless has grown considerably in the past few years but is it ready to embrace its maturity? And does that mean running away from NoOps or toward it? In the last part of our interview series, we invited six JAX DevOps speakers to weigh in on the serverless movement, its “competition” with container-based cloud infrastructure and the challenges Kubernetes and Docker should be addressing this year.
It’s finally here! Four months ago, the Docker team announced that they would add optional Kubernetes to Docker Community Edition for Mac and Windows and now we’re already seeing results. Docker for Windows with beta support for using Kubernetes as your orchestrator is now available.
What does the future of DevOps look like? We asked Mark Pundsack, Head of Product at GitLab about his predictions for 2018. Expect to hear lots about DevSecOps, Kubernetes, containers, and more.
The fourth and final Kubernetes release of 2017 is here. Kubernetes 1.9 brings a bunch of changes, including the fact that the Apps Workloads API is now stable. Let’s have a look at the highlights.
Kubeflow brings composable, easier to use stacks with more control and portability for Kubernetes deployments for all ML, not just TensorFlow.
It is always fascinating to see the versatile ways in which machine learning can be used. At Outfittery, algorithms help the experts select the most suitable outfits for customers — quite literally. In an interview at W-JAX 2017 in Munich, Jesper Richter-Reichhelm, CTO at Outfittery GmbH, explains how the company uses machine learning and which frameworks they use. He also tells us who makes better suggestions — human beings or machines.
The Docker platform and Moby Project are integrating support for Kubernetes. As of now (you can register for beta access here), developers and operators can build apps with Docker and seamlessly test and deploy them using both Docker Swarm and Kubernetes.
This release is *not* about introducing new features — instead, its aim is to strengthen existing ones. In short, Kubernetes 1.8 “represents a snapshot of many exciting enhancements and refinements underway.” Let’s see some of the highlights.
Looking for a serverless option for Kubernetes? Introducing Kubeless. This new Kubernetes-native serverless framework provides auto-scaling, API routing, and more without having to worry about the underlying infrastructure.
Google Container Engine is getting a Kubernetes upgrade. This latest collaboration between Google Cloud and Codefresh brings a faster, easier CI/CD pipeline for container deployment.
Moving from the monolith to microservices has a lot of advantages. In part two of this tutorial, Michael Gruczel finishes his step-by-step tutorial teaching developers how to implement microservices architecture in Kubernetes and Pivotal Cloud Foundry with Spring Boot.
Moving from the monolith to microservices has a lot of advantages. In part one of this tutorial, Michael Gruczel starts his step-by-step tutorial for developers who want to implement microservices architecture in Kubernetes and Pivotal Cloud Foundry with Spring Boot.
Kubernetes 1.7 focuses on three things: security, storage and extensibility features. There’s also a major feature that adds automated updates to StatefulSets and enhances updates for DaemonSets. Read on to find out what else is new.
