Author archive for Domino Data Lab, page 4
Domino Data Lab
Domino powers model-driven businesses with its leading Enterprise MLOps platform that accelerates the development and deployment of data science work while increasing collaboration and governance. More than 20 percent of the Fortune 100 count on Domino to help scale data science, turning it into a competitive advantage. Founded in 2013, Domino is backed by Sequoia Capital and other leading investors.
Domino Data Lab’s Data Science Evangelist named in DataIQ 100
DataIQ, the UK’s leading membership business for the data and analytics community, named David Bloch, Data Science Evangelist at Domino Data Lab, as one of the most influential leaders in the 2021 edition of the DataIQ 100.
By Domino Data Lab3 min read
Domino named a Visionary in Gartner Magic Quadrant
By Domino Data Lab on February 19, 2020
By Domino Data Lab6 min read
Kubernetes-native Domino Sets the Foundation for the Future
Embracing the future, Domino is now Kubernetes-native and ready to fluidly support innovations yet to come. The benefits of Kubernetes and the core values of Domino are solidly aligned - flexibility, reliability, cost reduction, and avoidance of vendor and tool lock-in. Knowing the importance that Kubernetes will play in Enterprise IT architecture in the next five years and beyond, our engineering team took on the task of fully replatforming Domino.
By Domino Data Lab4 min read
The Ethical Data Scientist: Three Leaders Share Their Vision for Data Responsibility
By Domino Data Lab on December 12, 2018 in
By Domino Data Lab5 min read
Big Data, Big Problems: Nate Silver of FiveThirtyEight Shares Tips for Navigating Today’s Data Science Challenges
When it comes to data, we assume that bigger is better. In fact, the age of Big Data brings a new array of challenges pertaining to data science modeling that today’s practitioners must tackle head-on, according to statistician Nate Silver, who founded FiveThirtyEight.com and is known for his analysis of political polls.
By Domino Data Lab5 min read
How Data Scientists Can Avoid Three Common Collaboration Challenges
For the vast majority of data science teams, math and coding prowess alone aren’t enough. Unless you’re working on an esoteric academic project, your skills will go to waste if you fail to cooperate with the colleagues that will end up using your products. The biggest challenges often come at the beginning and end of the data science workflow: understanding the problems you’re solving and making sure the results are put to use.
By Domino Data Lab6 min read
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