Subject archive for "data-science-leaders"

Two travelers using a tablet to help them with their trip while on a beach
Data Science Leaders

TripAdvisor's GenAI journey from billions of reviews to a virtual travel agent

See how TripAdvisor leverages GenAI to transform billions of reviews into a virtual travel agent tool.

By Yuval Zukerman5 min read

Enterprise MLOps

Ray Co-creator Robert Nishihara: How Easy Distributed Computing Changes Everything in Data Science

What if you wanted to do something really ambitious in data science–something like designing an innovative new search engine? Today, that would be a daunting task, and you’d probably need a big, highly qualified team of data scientists and programmers to bring your innovation to life. And you’d need months, if not years, to finish it.

By Lisa Stapleton3 min read

Enterprise MLOps

What the Rise of Data Science in Insurance Says About the Profession–and How It’s Changing

How do you build a data science capability into a powerful force for making decisions about virtually all facets of your business? And how do you recruit, train, organize, and retrain members of your team, in a field where competition for talent is intense and growing? These are just a couple of the questions that Glenn Hofmann, chief analytics officer at New York Life Insurance Company, has confronted and mastered in his tenure there.

By Lisa Stapleton4 min read

Data Science

7 Top Innovators Share Insights, Trends and Career Advice in 'The Data Science Innovator’s Playbook'

Who’s doing the most innovative things in data science? Where is the profession going? And most importantly, what can you learn from some of the brightest in the business?

By Lisa Stapleton6 min read

Perspective

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

Data Science

The Curse of Dimensionality

Danger of Big Data

By Bill Shannon14 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.