Subject archive for "data-science-leaders-at-work"
![](https://cdn.sanity.io/images/kuana2sp/production-main/eb1af102d05f825ebc1885bffbd63b59cb0bbd20-2000x1046.png?w=650&fit=max&auto=format)
NVIDIA’s Mona Flores: How Medical AI and Federated Learning Power Innovation
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
![](https://cdn.sanity.io/images/kuana2sp/production-main/635184e9e39bf16daf9e4710c5b73a109af2ef0c-1246x623.jpg?w=650&fit=max&auto=format)
How data science can fail faster to leap ahead
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
![](https://cdn.sanity.io/images/kuana2sp/production-main/ea65729fdce0c437ece92e40987df51bbfdfbf2f-4572x2803.jpg?w=650&fit=max&auto=format)
Themes and Conferences per Pacoid, Episode 5
In Paco Nathan's latest column, he explores the theme of "learning data science" by diving into education programs, learning materials, educational approaches, as well as perceptions about education.
By Paco Nathan27 min read
![](https://cdn.sanity.io/images/kuana2sp/production-main/423cac480484f82f93234823d72ebf4282166748-2560x1262.jpg?w=650&fit=max&auto=format)
Analyzing Large P Small N Data - Examples from Microbiome
Introduction
By Bill Shannon14 min read
![](https://cdn.sanity.io/images/kuana2sp/production-main/03a84463eeed41773c5571bcdcd1c156c1010cfe-2560x1312.jpg?w=650&fit=max&auto=format)
Evaluating Ray: Distributed Python for Massive Scalability
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
![](https://cdn.sanity.io/images/kuana2sp/production-main/2070e77893bb8479ba0b4f74994b318c29255ea8-3333x2679.jpg?w=650&fit=max&auto=format)
Themes and Conferences per Pacoid, Episode 12
Paco Nathan's latest monthly article covers Sci Foo as well as why data science leaders should rethink hiring and training priorities for their data science teams.
By Paco Nathan31 min read
Subscribe to the Domino Newsletter
Receive data science tips and tutorials from leading Data Science leaders, right to your inbox.
By submitting this form you agree to receive communications from Domino related to products and services in accordance with Domino's privacy policy and may opt-out at anytime.