Civo Integrations

Kubernetes (K8s) is an open-source system for automating deployment, scaling, and management of containerized applications. It groups containers that make up an application into logical units for easy management and discovery. Kubernetes builds upon 15 years of experience of running production workloads at Google, combined with best-of-breed ideas and practices from the community. Designed on the same principles that allows Google to run billions of containers a week, Kubernetes can scale without increasing your ops team. Whether testing locally or running a global enterprise, Kubernetes flexibility grows with you to deliver your applications consistently and easily no matter how complex your need is. Kubernetes is open source giving you the freedom to take advantage of on-premises, hybrid, or public cloud infrastructure, letting you effortlessly move workloads to where it matters to you.

The self-service developer platform for your apps, databases, and jobs. Start with one workload, and scale to hundreds on compute or GPUs. Accelerate every step from push to production with highly configurable self-service workflows, pipelines, templates, and GitOps. Securely deploy preview, staging, and production environments with observability tooling, backups, restores, and rollbacks included. Northflank seamlessly integrates with your preferred tooling and can accommodate any tech stack. Whether you deploy on Northflank’s secure infrastructure or on your own cloud account, you get the same exceptional developer experience, and total control over your data residency, deployment regions, security, and cloud expenses. Northflank leverages Kubernetes as an operating system to give you the best of cloud-native, without the overhead. Deploy to Northflank’s cloud for maximum simplicity, or connect your GKE, EKS, AKS, or bare-metal to deliver a managed platform experience in minutes.

The Kubeflow project is dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. Our goal is not to recreate other services, but to provide a straightforward way to deploy best-of-breed open-source systems for ML to diverse infrastructures. Anywhere you are running Kubernetes, you should be able to run Kubeflow. Kubeflow provides a custom TensorFlow training job operator that you can use to train your ML model. In particular, Kubeflow's job operator can handle distributed TensorFlow training jobs. Configure the training controller to use CPUs or GPUs and to suit various cluster sizes. Kubeflow includes services to create and manage interactive Jupyter notebooks. You can customize your notebook deployment and your compute resources to suit your data science needs. Experiment with your workflows locally, then deploy them to a cloud when you're ready.