Subject archive for "data-scientists," page 2
Building models requires a lot of time and effort. Data scientists can spend weeks just trying to find, capture and transform data into decent features for models, not to mention many cycles of training, tuning, and tweaking models so they’re performant.
By David Bloch9 min read
Today, we announced the latest release of Domino’s data science platform which represents a big step forward for enterprise data science teams. We’re introducing groundbreaking new features – including On-demand Spark clusters, enhanced project management, and the ability to export models – that give enterprises unprecedented power to scale their data science capabilities by addressing common struggles.
By Nick Elprin11 min read
This Domino Data Science Field Note covers Chris Wiggins's recent data ethics seminar at Berkeley. The article focuses on 1) proposed frameworks for defining and designing for ethics and for understanding the forces that encourage industry to operationalize ethics, as well as 2) proposed ethical principles for data scientists to consider when developing data-empowered products. Many thanks to Chris for providing feedback on this post prior to publication and for the permission to excerpt his slides.
By Ann Spencer12 min read
Chris Wiggins, Chief Data Scientist at The New York Times, presented "Data Science at the New York Times" at Rev. Wiggins advocated that data scientists find problems that impact the business; re-frame the problem as a machine learning (ML) task; execute on the ML task; and communicate the results back to the business in an impactful way. He covered examples of how his team addressed business problems with descriptive, predictive, and prescriptive ML solutions. This post provides distilled highlights, a transcript, and a video of the session. Many thanks to Chris Wiggins for providing feedback on this post prior to publication.
By Ann Spencer40 min read
Our last release, Domino 3.3 saw the addition of two major capabilities: Datasets and Experiment Manager. “Datasets”, a high-performance, revisioned data store offers data scientists the flexibility they need to make use of large data resources when developing models. And “Experiment Manager” acts as a data scientist’s “modern lab notebook” for tracking, organizing, and finding everything tested over the course of their research.
By Domino2 min read
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