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Data Science Success All About the Models, Domino Says

#artificialintelligence

Forget about being "data driven." What you really want to be is "model driven," according to the CEO of Domino Data Lab, which today unveiled its new vision for elevating the predictive model as the single most important asset driving success in data science organizations. Nick Elprin co-founded Domino Data Lab with two colleagues, Chris Yang and Matthew Granade, at the height of the big data boom in 2013. With experience working as quants in the financial services industry, the founders were eager to build a platform that could help organizations build systems to leverage their data to gain a competitive edge, no matter the industry. At first, Domino focused on lowering the barrier separating data scientists from utilizing parallel computational infrastructure.


Q&A with Domino Data Lab's CEO

@machinelearnbot

The field of data science is fairly young and evolving extremely rapidly. Finding people who can harness the tornado of big data tech is a major challenge. One of the up and coming vendors who are making data science more accessible is Domino Data Lab. Datanami recently talked with Nick Elprin, the co-founder and CEO of Domino Data Lab, a data science software company based in San Francisco. Here is an edited transcript of the conversation.


Domino Data Lab Brings Data Science Platform to Kubernetes - Container Journal

#artificialintelligence

Domino Data Lab is making the case for a multi-cloud approach to building and deploying applications infused with machine learning algorithms now that its platform runs on Kubernetes. Company CEO Nick Elprin says that as organizations move to employ machine learning algorithms to build various types of applications, many of them don't appreciate the extent to which relying on proprietary services is locking them into a "walled garden" that only runs on a specific cloud computing platform. Many of those same organizations may even wake up one morning to discover they are suddenly now competing with Amazon, Google or Microsoft, all of which are rapidly expanding the type of services they provide based on machine learning algorithms, he notes. By opting to build machine learning models on a platform provided by Domino Data Lab, organizations can deploy those models on any public cloud or on-premises IT environment as they best see fit, Elprin says. Longer-term, Domino Data Labs is betting most applications employing machine learning algorithms also will be likely to span multiple clouds, he adds.


Open Data Science Company Domino Data Lab Secures $40M FinSMEs

#artificialintelligence

Domino Data Lab, a San Francisco, CA-based provider of an open data science platform, secured $40m in funding round. The round, which brought total capital raised to $81.2m, was led by Sequoia Capital and Coatue Management. The company intends to use the funds to continue to deliver new data science platform products, acquire talent, expand globally, and secure and deepen strategic partnerships with companies like SAS and AWS. Founded in 2013 by Nick Elprin, CEO, Domino Data Lab provides an open data science platform for companies to run their business on models. Model-driven companies like Allstate, Instacart, Dell, and Monsanto use the company's platform to accelerate research, increase collaboration, and deliver high-impact models.


Data Science: 4 Reasons Why Most Are Failing to Deliver

@machinelearnbot

One of today's organizational dilemmas: it's pretty well understood that data science is a key driver of innovation, but few organizations know how to consistently turn data science output into business value. Sixty percent of companies plan to double the size of their data science teams in 2018. Ninety percent believe data science contributes to business innovation. However, less than nine percent can actually quantify the business impact of all their models, and only 11 percent can claim more than 50 predictive models working in production. This data stems from a recent survey of more than 250 data science leaders and practitioners.