Kubeflow vs Xcessiv
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A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. | 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. |
Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code;Any model following the Scikit-learn API can be used as a base learner;Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches;Direct integration with TPOT for automated pipeline construction;Automated hyperparameter search through Bayesian optimization;Easy management and comparison of hundreds of different model-hyperparameter combinations;Automatic saving of generated secondary meta-features;Stacked ensemble creation in a few clicks;Automated ensemble construction through greedy forward model selection;Export your stacked ensemble as a standalone Python file to support multiple levels of stacking | - |
Statistics | |
GitHub Stars 1.3K | GitHub Stars - |
GitHub Forks 105 | GitHub Forks - |
Stacks 0 | Stacks 205 |
Followers 7 | Followers 585 |
Votes 0 | Votes 18 |
Pros & Cons | |
No community feedback yet | Pros
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What are some alternatives to Xcessiv, Kubeflow?
TensorFlow
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scikit-learn
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PyTorch
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Keras
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Streamlit
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MLflow
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H2O
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PredictionIO
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