Why You See What You Like: Machine Learning In Modern Publishing


Machine learning systems have permeated nearly every aspect of our digital lives. We live in a time dominated by applications guiding our decisions and serving us content selected for us by a model somewhere.

What goes into building such systems at scale? We want to offer a behind-the-scenes look at how Conde Nast has led the publishing industry into the digital era by adopting a data-driven approach to content and audience intelligence.

Have you ever read an article in the New Yorker? Wired? Ever liked a post by Vogue on Twitter or Facebook? Then you’ve probably seen our machine learning systems in action.

We will talk about ML engineering and operations behind these systems at Conde Nast. How did we build an audience segmentation platform that helps advertisers serve relevant ads? How do we recommend articles for personalized newsletters? How do we determine whether a social media post is likely to go viral?

We will focus on the general design of systems that enable such cool things. Specifically, we will delve into why we chose different types of infrastructures for different projects and some of the challenges we faced when developing scalable and reliable machine learning systems.

We will focus on how we build systems that adapt well to changing data and the key components necessary for the maintainability of such systems. Additionally, we will also talk about how we structure and design the core abstractions of a system in a modular manner to enable more flexibility and faster development.

Through this talk, we want to shed light on how machine learning can empower a publishing company spread out across various geographies, genres, and brands, directly or indirectly, by improving key business metrics.

Hopefully, our learnings can help other ML engineers choose better infrastructures and designs for their systems and eliminate some common pain points.


Hariprasath Thiagarajan leads the ML Product development and MLOps for various business verticals such as recommendations and subscriptions within Condé Nast across the global market, catering to different business use cases. Hariprasath has experience building software products for over 10+ years on different domains using Java, Scala, and Python. His involvement with Condé Nast allowed him to build most of the products from the ground up. Hari, and his team, specialize in scaling the process and architecture of these products so that the time-to-market for new brands is relatively lesser.

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