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 a live webinar on Thursday, June 21, at 2PM CT, 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.
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Tom Augspurger, Data Scientist
Tom 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 larger datasets and larger models.
With over 6 million users, Anaconda is the world’s most popular Python data science platform. Anaconda, Inc. continues to lead open source projects like Anaconda, NumPy and SciPy that form the foundation of modern data science. Anaconda’s flagship product, Anaconda Enterprise, allows organizations to secure, govern, scale and extend Anaconda to deliver actionable insights that drive businesses and industries forward.