Subject archive for "data-science-leaders-at-work"
What if machine learning and data scattered around the world held the keys to the cures for a variety of rare or new diseases? Until recently, it was often nearly impossible to use that data, due in part to the data privacy restrictions in place around the world. That’s starting to change, thanks to federated learning and the innovative work of doctors such as Mona Flores, head of medical AI at NVIDIA.
By Lisa Stapleton4 min read
One of the biggest challenges in data science today is finding the right tool to get the job done. The rapid change in best-in-class options makes this especially challenging - just look at how quickly R has fallen out of favor while new languages pop up. If data science is to advance as rapidly as possible in the enterprise, scientists need the tools to run multiple experiments quickly, discard approaches that aren’t working, and iterate on the best remaining options. Data scientists need a workspace where they can easily experiment, fail quickly, and determine the best data solution before they run a model through certification and deployment.
By Nikolay Manchev8 min read
By Bill Shannon14 min read
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.
By Dean Wampler14 min read
This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing for ethics and for understanding the forces that encourage industry to operationalize ethics, as well as 2) proposed ethical principles for data scientists to consider when developing data-empowered products. Many thanks to Chris for providing feedback on this post prior to publication and for the permission to excerpt his slides.
By Ann Spencer12 min read
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