Kubeflow
The ML toolkit that turns a Kubernetes cluster into an AI platform.
Kubeflow brings the ML lifecycle to Kubernetes: Pipelines orchestrates training workflows as containerized DAGs, Notebooks provides isolated Jupyter environments, Katib automates hyperparameter tuning and KServe serves models with autoscaling. Multi-user with per-namespace isolation, it's our choice to industrialize a team's ML on an existing cluster, cloud or on-premise, without vendor lock-in.
What Kubeflow brings to your project.
Typical use cases: Shared ML platform on Kubernetes, distributed training, serving.
- 01
Pipelines: containerized, versioned, replayable ML workflows.
- 02
Katib: distributed hyperparameter tuning and NAS on the cluster.
- 03
KServe: model serving with autoscaling, canary and scale-to-zero.
- 04
Multi-user: isolated notebooks, RBAC and quotas per namespace.
Entrust your project
to our experts.
Entrust your project to our
experts.
Entrust your project
to our experts.
Our experts build your project, delivering superior technical and functional quality within shorter timeframes.
They trust us.
They trust
us.
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A project with Kubeflow?
Describe your project. Our team replies within 24 hours with free technical scoping, along with a clear estimate of costs and timelines. No commitment.
- Reply within 24 hours from a project manager
or engineer.Reply within 24 hours from a project manager or engineer. - Technical scoping and quote, with no fees.
- No commitment, your data stays
confidential.No commitment, your data stays confidential.
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Free scoping & estimate in less than 24h
Describe your project and we'll get back to you with a costed estimate and a roadmap.