AnacondaCon 2018. Tom Augspurger. Scikit-Learn, NumPy, and pandas form a great toolkit for single-machine, in- memory analytics. Scaling them to larger datasets can be difficult, as you have to adjust your workflow to use chunking or incremental learners. Dask provides NumPy- and pandas-like data containers for manipulating larger than memory datasets, and dask-ml provides estimators and utilities for modeling larger than memory datasets. These tools scale your usual workflow out to larger datasets. We’ll discuss some of the challenges data scientists run into when scaling out to larger datasets. We’ll then focus on demonstrations of how dask and dask-ml solve those challenges. We’ll see examples of how dask can expose a cluster of machines to scikit-learn’s built-in parallelization framework. We’ll see how dask-ml can train estimators on large datasets.
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