Subject archive for "data-science-platform," page 2

Data Science

The Past/Present/Future + Myths of Data Science

Sivan Aldor-Noiman, VP of Data Science at Wellio (now part of The Kraft Heinz Company), presented “The Past/Present/Future + Myths of Data Science” at Domino. This blog post provides a few highlights from the interactive talk as well as the full video.

By Domino4 min read

Data Science

Achieving Reproducibility with Conda and Domino Environments

Managing “environments” (i.e., the set of packages, configuration, etc.) is a critical capability of any Data Science Platform. Not only does environment setup waste time on-boarding people, but configuration issues across environments can undermine reproducibility and collaboration, and can introduce delays when moving models from development to production.

By Eduardo Ariño de la Rubia8 min read

Data Science

Our Customers Said it Best, as Domino Named a Visionary By Gartner

The team at Domino is proud to be named a visionary in Gartner’s Magic Quadrant for Data Science Platforms. It’s nice to get recognized by third parties, but it was particularly gratifying to hear feedback from our customers synthesized and channeled through Gartner’s report. We were most proud of the feedback we got in three areas: our product, our customer service, and our sales team.

By Nick Elprin2 min read

Data Science

Enabling Data Science Agility with Docker

This post describes how Domino uses Docker to solve a number of interconnected problems for data scientists and researchers, related to environment agility and reproducibility of work.

By Nick Elprin9 min read

Data Science

Domino raises $10.5M in funding for collaborative, reproducible data science

Today we’re announcing that we have raised $10.5 million in a funding round led by Sequoia Capital.

By Nick Elprin4 min read

Data Science

Data Science Platform: What is it? Why is it Important?

As more companies recognize the need for a [data science platform], more vendors are claiming they have one. Increasingly, we see companies describing their product as a “data science platform” without describing the features that make platforms so valuable. So we wanted to share our vision for the core capabilities a platform should have in order for it to be valuable to data science teams.

By Nick Elprin7 min read

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