  In the recent years, social media have become one of the main places where
creative content is being published and consumed by billions of users. Contrary
to traditional media, social media allow the publishers to receive almost
instantaneous feedback regarding their creative work at an unprecedented scale.
This is a perfect use case for machine learning methods that can use these
massive amounts of data to provide content creators with inspirational ideas
and constructive criticism of their work. In this work, we present a
comprehensive overview of machine learning-empowered tools we developed for
video creators at Group Nine Media - one of the major social media companies
that creates short-form videos with over three billion views per month. Our
main contribution is a set of tools that allow the creators to leverage massive
amounts of data to improve their creation process, evaluate their videos before
the publication and improve content quality. These applications include an
interactive conversational bot that allows access to material archives, a
Web-based application for automatic selection of optimal video thumbnail, as
well as deep learning methods for optimizing headline and predicting video
popularity. Our A/B tests show that deployment of our tools leads to
significant increase of average video view count by 12.9%. Our additional
contribution is a set of considerations collected during the deployment of
those tools that can hel
