Subject archive for "data-science," page 10

Dean Wampler provides a distilled overview of Ray, an open source system for scaling Python systems from single machines to large clusters. If you are interested in additional insights, register for the upcoming Ray Summit.

This article provides concise insights into GANs to help data scientists and researchers assess whether to investigate GANs further. If you are interested in a tutorial as well as hands-on code examples within a Domino project, then consider attending the upcoming webinar, “Generative Adversarial Networks: A Distilled Tutorial”.

This article covers how to detect data drift for models that ingest image data as their input in order to prevent their silent degradation in production.

Last week we announced that Domino is now fully Kubernetes native.

This Domino Data Science Field Note covers a proposed definition of interpretability and distilled overview of the PDR framework. Insights are drawn from Bin Yu, W. James Murdoch, Chandan Singh, Karl Kumber, and Reza Abbasi-Asi's recent paper, "Definitions, methods, and applications in interpretable machine learning".

Paco Nathan's latest article covers data practices from the National Oceanic and Atmospheric Administration (NOAA) Environment Data Management (EDM) workshop as well as updates from the AI Conference.