Kubeflow is an open source platform that makes it easy, portable, and scalable to deploy machine learning (ML) workflows on Kubernetes. It provides an ML stack for Kubernetes, consisting of TensorFlow, Jupyter notebooks and other ML tools. With Kubeflow, you can build, train, and deploy machine learning models consistently across environments. Kubeflow enables you to run end to end ML workflows on Kubernetes, from data preparation to model training and deployment, using the same infrastructure and tools. It simplifies the deployment process by abstracting away the complexities of managing Kubernetes resources, allowing you to focus on developing and iterating ML models.
Deploy Kubeflow on Kubernetes via Helm.
Build training and deployment workflows with Kubernetes-native tooling.
Coordinate data prep, training, and model delivery stages.
Provide shared tooling for data scientists and ML engineers.
Additional Nebius infrastructure costs may apply. Use the Nebius Pricing Page to estimate your infrastructure costs.
Deploy Kubeflow on Kubernetes.
Some applications are easier with a hand on the wheel. Talk to an architect who has deployed this in production.
Reliable AI infrastructure backed by top-tier NVIDIA GPUs, purpose-built for demanding inference workloads. Multiple deployment methods — virtual machines for full hardware control, Kubernetes for scalable cluster deployments, and managed serverless applications for teams that want inference running without infrastructure overhead
Learn about Nebius AI CloudNebius meets a broad set of security and compliance standards. Fine-grained IAM controls, audit logs, and encrypted storage are available out of the box — so teams can meet security requirements without additional tooling.
Explore the Trust centerApplication support
Provided by Aranui Solutions. See the documentation and project links above.
Infrastructure support
Provided by Nebius for the underlying cloud infrastructure.
