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

Before we get too far into 2019, I wanted to take a brief moment to reflect on some of the changes we’ve seen in the market. In 2018 we saw the “data science platform” market rapidly crystallize into three distinct product segments. This post describes our observations about these three segments and offers advice for folks evaluating products in this space.

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.

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.

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.

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.

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