Subject archive for "model-management," page 5

This Domino Data Science Field Note provides highlights and excerpted slides from Chloe Mawer’s "The Ingredients of a Reproducible Machine Learning Model" talk at a recent WiMLDS meetup. Mawer is a Principal Data Scientist at Lineage Logistics as well as an Adjunct Lecturer at Northwestern University. Special thanks to Mawer for the permission to excerpt the slides in this Domino Data Science Field Note. The full deck is available here.

Paco Nathan's latest column dives into data governance.

Our mission at Domino is to enable organizations to put models at the heart of their business. Models are so different from software — e.g., they require much more data during development, they involve a more experimental research process, and they behave non-deterministically — that organizations need new products and processes to enable data science teams to develop, deploy and manage them at scale.

Paco Nathan covers recent research on data infrastructure as well as adoption of machine learning and AI in the enterprise.

In Paco Nathan's latest column, he explores the role of curiosity in data science work as well as Rev 2, an upcoming summit for data science leaders.

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.