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Speed Cubing for Machine Learning - Episode 1

N. Morizet, Towards Data Science, September 15th, 2020.

Abstract: At some point in a machine learning project, you will want to overcome the limitations of your local machine (number of cores, memory, etc.), whether you want to generate a large amount of data to feed deep learning networks or train your algorithms as fast as possible. In this article, we will take you on a journey, sharing the difficulties and the solutions we found, from naive implementation to more advanced techniques, taking advantage of the various computational resources that may be found in the Cloud. We hope this will help you to efficiently manage and improve your productivity for your machine learning projects.


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