Like any company, Automattic is constantly on a journey to get better: sometimes we have the good fortune of finding improvement in leaps and bounds, but most of the time, we move slowly, we make small changes, finding iterative wins and moving down the to-do list. I think probably this is how most progress happens: … Continue reading Looker NYC Meetup
This week, Carly, Demet, and Charles bring you some interesting material on tech and the humanities, experimentation culture, and eliminating bias in testing.
Charles Earl shares what he learned from this year's conference.
This week, Boris, Xiao, and Carly share recent reads about MOOCs, collusion in AI pricing, and generating fake news with artificial intelligence.
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.
Richard Brath talks to Automattic data visualization enthusiasts about the power of text in conveying the results of data science.