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Comet.ml vs Kubeflow: What are the differences?
Developers describe Comet.ml as "Track, compare and collaborate on Machine Learning experiments". Comet.ml allows data science teams and individuals to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility. On the other hand, Kubeflow is detailed 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.
Comet.ml and Kubeflow can be categorized as "Machine Learning" tools.
Kubeflow is an open source tool with 6.93K GitHub stars and 1K GitHub forks. Here's a link to Kubeflow's open source repository on GitHub.
Pros of Comet.ml
- Best tool for comparing experiments3
Pros of Kubeflow
- System designer9
- Google backed3
- Customisation3
- Kfp dsl3
- Azure0
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What is Comet.ml?
What is Kubeflow?
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What tools integrate with Comet.ml?
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