Kubeflow vs MLflow: 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, MLflow is detailed as "An open source machine learning platform". MLflow is an open source platform for managing the end-to-end machine learning lifecycle.
Kubeflow and MLflow can be categorized as "Machine Learning" tools.
Kubeflow and MLflow are both open source tools. It seems that Kubeflow with 6.93K GitHub stars and 1K forks on GitHub has more adoption than MLflow with 20 GitHub stars and 11 GitHub forks.
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What is Kubeflow?
What is MLflow?
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