Subject archive for "data-science-leaders-at-work," page 2
Even the most sophisticated data science organizations struggle to keep track of their data science projects. Data science leaders want to know, at any given moment, not just how many data science projects are in flight but what the latest updates and roadblocks are when it comes to model development and what projects need their immediate attention.
By Domino8 min read
This Domino Data Science Field Note covers Pete Skomoroch’s recent Strata London talk. It focuses on his ML product management insights and lessons learned. If you are interested in hearing more practical insights on ML or AI product management, then consider attending Pete’s upcoming session at Rev.
By Ann Spencer8 min read
Eugene Mandel, Head of Product at Superconductive Health, recently dropped by Domino HQ to candidly discuss cross-team collaboration within data science. Mandel’s previous leadership roles within data engineering, product, and data science teams at multiple companies provides him with a unique perspective when identifying and addressing potential tension points.
By Ann Spencer35 min read
This blog post provides insights into why machine learning teams have challenges with managing machine learning projects. He also provides best practices on how to address these challenges. This post was provided courtesy of Lukas and originally appeared on Medium.
By Lukas Biewald9 min read
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