Cameron Davidson-Pilon talks to the Automattic data scientists about his work post-Shopify, data practices, and how data scientists can best serve their organizations.
Charles Earl shares what he learned from this year's conference.
Yanir reflects on how data scientists at Automattic work to improve customer retention.
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
Demet gives us an overview of pipe, Automattic's machine learning pipeline.
This week, Charles shares a couple of talks he enjoyed on neural networks at Georgia Tech's Theoretical Foundation of Deep Learning conference.
Check out Demet's data-driven analysis of communication at Automattic.