Kubeflow vs Pipelines: What are the differences?
Developers describe Kubeflow as "Machine Learning Toolkit for Kubernetes". The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions. On the other hand, Pipelines is detailed as "Machine Learning Pipelines for Kubeflow". Kubeflow is a machine learning (ML) toolkit that is dedicated to making deployments of ML workflows on Kubernetes simple, portable, and scalable. Kubeflow pipelines are reusable end-to-end ML workflows built using the Kubeflow Pipelines SDK.
Kubeflow and Pipelines belong to "Machine Learning Tools" category of the tech stack.
Kubeflow and Pipelines are both open source tools. Kubeflow with 7.04K GitHub stars and 1.03K forks on GitHub appears to be more popular than Pipelines with 944 GitHub stars and 247 GitHub forks.
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What is Kubeflow?
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