Author archive for Nick Elprin, page 5

Nick Elprin

Nick Elprin is the CEO and co-founder of Domino Data Lab, provider of the open data science platform that powers model-driven enterprises such as Allstate, Bristol Myers Squibb, Dell and Lockheed Martin. Before starting Domino, Nick built tools for quantitative researchers at Bridgewater, one of the world's largest hedge funds. He has over a decade of experience working with data scientists at advanced enterprises. He holds a BA and MS in computer science from Harvard.

Data Science

Better Interactive Data Science with Beaker and Rodeo

Domino has offered support for IPython/Jupyter Notebook for a while, but we recently added support for two newer, up-and-coming tools for interactive data science: Beaker Notebooks, and Rodeo. This post gives a brief overview of each tool and describes how to use them on Domino.

By Nick Elprin4 min read

Data Science

To Jupyter and Beyond

TLDR; Domino now supports Jupyter with R, Python, and Julia kernels as well as terminal access. It now renders ipynb files in the browser, letting you more easily share, compare, and discuss notebooks; and it lets you run or schedule notebooks as batch jobs, making notebooks a great reporting tool.

By Nick Elprin4 min read

Data Science

Faster model tuning and experimentation

Domino provides a great way to iterate on analytical models by letting you run many experiments in parallel on powerful hardware and automatically track their results. Recently we added two powerful new features to make that workflow even easier: Run Comparisons and Diagnostic Statistics.

By Nick Elprin4 min read

Data Science

Reflections on "Buy vs Build" for Data Science Tools

“Buy vs build”, “not-invented-here syndrome” and even “invented-here-syndrome” have been written about extensively. I want to share a few reflections on the topic, based on my observations both as an engineering manager (where I had to decide whether to build or buy solutions) and more recently as a founder selling a platform to other companies.

By Nick Elprin11 min read

Data Science

Building an Open Product for Power Users

This post describes our engineering philosophy of building an “open” product, i.e., one that supports existing tools and libraries, rather than building our own custom version of existing functionality. Aside from letting our developers be more productive, we’ve found this approach makes our users much more productive — especially power users, who are especially important to us.

By Nick Elprin8 min read

Data Science

10 Interesting Ways to Use Data Science

There's buzz everywhere these days about exciting applications of data science, especially related to machine learning. While the majority of the media attention focuses on big names and sexy topics — e.g., the Google Brain, image classification, speech recognition — we are fortunate to work with companies every day that are doing fascinating work with data science tools. Although sometimes more mundane, many of their use cases have more real-world impact.

By Nick Elprin6 min read

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