Streamz helps you build pipelines to manage continuous streams of data. It is simple to use in simple cases, but also supports complex pipelines that involve branching, joining, flow control, feedback, back pressure, and so on.
Optionally, Streamz can also work with both Pandas and cuDF dataframes, to provide sensible streaming operations on continuous tabular data.
To learn more about how to use Streamz see documentation at streamz.readthedocs.org.
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Machine Learning Conference & Summer School Notes. 🦄📝🎉
nbdime provides tools for diffing and merging of Jupyter Notebooks.
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The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machine Learning Experiment (MLE) pipeline.
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The Ultimate FREE Machine Learning Study Plan
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