Scaling machine learning with clusters and GPUs (Ep. 134)

<p>Let's finish this year with an amazing episode about scaling ML with clusters and GPUs. Kind of as a continuation of <a href='https://datascienceathome.com/what-data-transformation-library-should-i-use-pandas-vs-dask-vs-ray-vs-modin-vs-rapids-ep-112/'>Episode 112</a> I have a terrific conversation with Aaron Richter from Saturn Cloud about, well, making ML faster and scaling it to massive infrastructure.</p> <p>Aaron can be reached on his website <a href='https://rikturr.com'>https://rikturr.com</a> and Twitter <a href='https://twitter.com/rikturr'>@rikturr</a></p> <p> </p> <p>Our Sponsor</p> <p><a href='https://saturncloud.io'>Saturn Cloud</a> is a data science and machine learning platform for scalable Python analytics. Users can jump into cloud-based Jupyter and Dask to scale Python for big data using the libraries they know and love, while leveraging Docker and Kubernetes so that work is reproducible, shareable, and ready for production.</p> <p>Try Saturn Cloud for free at <a href='https://saturncloud.io'>https://saturncloud.io</a> </p> <p>Twitter: <a href='https://twitter.com/saturn_cloud'>@saturn_cloud</a></p> <p> </p> <p> </p>