Description
At OpBandit, we built a Python Twisted service that renders different versions of news content for rendering on top publishers across six countries. At a high level, whenever a reader requests a page on a publisher's website, our service selects from various versions of headlines and photos to deliver the collection of versions that we think a user is most likely to click. This requires decision making on the fly for each request with hard requirements for speed and reliability. This talk will cover how we used Twisted to scale our service to provide hundreds of millions of optimized pages / month for readers, as well as what it's like to attempt to scale real-time data science recommendation algorithms within Python.