Demet on using pipe to segment and study how users respond to marketing email campaigns.
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Data Science Insights from Cameron Davidson-Pilon
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.
… Continue readingThe 2019 Fairness, Accountability, and Transparency Conference
Charles Earl shares what he learned from this year's conference.
… Continue readingHow to Increase Retention and Revenue in 1,000 Nontrivial Steps
Yanir reflects on how data scientists at Automattic work to improve customer retention.
… Continue readingBuilding Thousands of Reproducible ML Models with pipe, the Automattic Machine Learning Pipeline
Demet takes you deep into pipe, a tool that allows anyone at Automattic to build solid machine learning models.
… Continue readingIntroducing pipe, The Automattic Machine Learning Pipeline
Demet gives us an overview of pipe, Automattic's machine learning pipeline.
… Continue readingLinks Worth Sharing: The Training and Security of Neural Networks
This week, Charles shares a couple of talks he enjoyed on neural networks at Georgia Tech's Theoretical Foundation of Deep Learning conference.
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