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SUMMARY:Hands-on Kubernetes as a User
DESCRIPTION:Registration is closed for this event\nAbout the event\nThis training is organized in collaboration with ENCCS.  \nKubernetes is the backbone of modern infrastructure\, being used for deployment of all sorts of applications. Nowadays\, knowing how to use it is an important skillset for those interested in artificial intelligence\, data science\, and MLOps. \nIn this highly practical workshop\, you will learn how to use Kubernetes from a user perspective. The course combines short theoretical introductions with guided hands-on exercises where you will configure a local cluster in your computer\, deploy services\, and run real workloads. By the end of the workshop\, you will have a working environment on your own machine and practical experience deploying interactive tools\, monitoring solutions\, and AI workloads. \nWho is this for?\nThis workshop is intended for: \n\n\nAI/MLOps engineers\, Researchers\, data scientists working with AI\, machine learning\, data science\, or scientific computing \n\n\nUsers of HPC or cloud environments who want to understand Kubernetes-based platforms and deploy their own stack without waiting for an infrastructure team. \n\n\nAnyone interested in MLOps\, reproducible workflows\, and scalable application deployment \n\n\nBeginners to Kubernetes who want a practical\, user-level introduction \n\n\nNo prior Kubernetes experience is required. \nKey takeaways for participants\n\n\nUnderstand what Kubernetes is\, its architecture\, core components\, and different deployment flavors\, including how it is used in multi-tenant environments (e.g.\, LUMI-K) \n\n\nLearn important Kubernetes concepts such as namespaces\, pods\, deployments\, jobs\, storage\, and scaling \n\n\nUnderstand the Kubernetes networking model\, including services\, ingress\, and load balancing \n\n\nBe able to read\, write\, and modify YAML manifests to deploy and manage applications \n\n\nDeploy and use JupyterLab for interactive computing inside a Kubernetes cluster \n\n\nSet up Prometheus and Grafana to monitor cluster resources and workloads \n\n\nDeploy a simple AI model and quickly track experiments using MLflow \n\n\nGain practical experience working with a local Kubernetes cluster on your own machine \n\n\nPrerequisites\n\n\nParticipants should: \n\n\nHave a laptop with Windows (WSL)\, macOS\, or Linux \n\n\nHave administrative privileges on the system \n\n\nInstall the required tools before the workshop (instructions will be provided) \n\n\nBasic knowledge of containers (e.g.\, Docker) is helpful but not required. Relevant concepts will be introduced during the workshop. \n\n\nNotes\nThis is a 5-hour hands-on workshop focused on practical usage. The course will not cover: \n\n\nKubernetes API development \n\n\nCluster installation or administration in production environments \n\n\nAdvanced operational or DevOps topics \n\n\nThe goal is to provide a solid user-level foundation for running applications and AI workloads on Kubernetes. \nSchedule\n\n\n\n28 April 2026\nContents\n\n\n\n\n13:00 – 16:00\nConfiguring a local cluster in your computer (Optional)\n\n\n\n\n29 April 2026\n\n\n\n\n\n09:00 – 09:40\nIntroduction to Kubernetes: Architecture\, Components and Flavors (Theory)\n\n\n09:40 – 10:50\nIntroduction to Kubernetes: Concepts (Theory)\n\n\n10:50 – 11:00\nShort Break\n\n\n11:00 – 12:00\nHands-on 1: Deploying Jupyterhub\, Prometheus and Grafana\n\n\n12:00 – 13:15\nLunch break\n\n\n13:15 – 14:45\nHands-on 2: Deploying an AI model and MLflow\n\n\n14:45 – 15:00\nQ&A session\n\n\n\n 
URL:https://mimer-ai.eu/event/hands-on-kubernetes-as-a-user/
ATTACH;FMTTYPE=image/jpeg:https://mimer-ai.eu/wp-content/uploads/2026/03/mi.jpg
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