This talk demonstrates how to scale a Python-based machine learning workflow to larger models and larger datasets. The talk will introduce a common workflow using NumPy, pandas, and scikit-learn, and discuss some challenges with scaling that workflow out to larger datasets. We'll then see how dask and dask-ml work with and extend these libraries to enable large-scale parallel and distributed machine learning.
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