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Kubeflow is an open-source platform for developing, orchestrating, and deploying machine learning workloads on Kubernetes. It provides reusable pipelines, model serving, distributed training support, monitoring, and integrations with cloud Kubernetes offerings. Kubeflow enables scalable, cloud-ready MLOps workflows but requires Kubernetes and platform engine…
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Kubeflow is a Kubernetes-based platform that helps teams organize machine learning from development and training runs through to repeatable deployment workflows.
Kubeflow began at Google in 2017 to address the problem of making TensorFlow workloads accessible and portable on Kubernetes. The project grew in the Kubernetes community into a platform supporting multiple ML frameworks.
Think of an ML workshop running on Kubernetes: notebooks and components are workstations, pipelines connect processing steps, training jobs consume compute, and serving exposes models as services. Kubernetes provides scheduling, scaling, and resilience underneath the workshop.
They describe ML steps as repeatable, versioned workflows.
Training jobs run experiments with data and compute resources.
It exposes trained models as services for predictions.
Kubeflow helps when ML teams need reproducible experiments, shared GPU and cluster resources, or controlled model operations. Kubernetes operations, data versioning, and observability for long-running pipelines remain important responsibilities.
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