Scalable Machine Learning with Dask
Scikit-Learn, NumPy, and pandas form a great toolkit for single-machine, in-memory analytics, but scaling them to larger datasets can be difficult.
In this webinar, watch Anaconda Data Scientist Tom Augspurger demonstrate how dask enables analysis of large datasets in parallel, using all the cores of your laptop or all the machines in your cluster.
Tom will highlight dask-ml, a library for scalable machine learning, and show you how dask-ml can train estimators on large datasets.
In this webinar, watch Anaconda Data Scientist Tom Augspurger demonstrate how dask enables analysis of large datasets in parallel, using all the cores of your laptop or all the machines in your cluster.
Tom will highlight dask-ml, a library for scalable machine learning, and show you how dask-ml can train estimators on large datasets.
Meet Our Speaker
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Tom Augspurger, Data ScientistTom is a Data Scientist and developer at Anaconda and works on open source projects including dask and pandas. Tom’s current focus is on scaling out Python’s machine learning ecosystem to largerdatasets and larger models.
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