Harin Sanghirun

Machine Learning Engineer, Condé Nast

Harin is a Machine Learning Engineer at Condé Nast where he researches and productionizes machine learning models in the domain of content and advertising recommendations. He has adopted cutting-edge Deep Learning models in Computer Vision and Natural Language Processing to bring recommendations to Condé Nast global audience. He received his Masters in Data Science from Columbia University and Bachelor of Engineering in Computer Engineering from Chulalongkorn University, Thailand.

Past sessions

Condé Nast is a global leader in the media production space housing iconic brands such as The New Yorker, Wired, Vanity Fair, and Epicurious, among many others. Along with our content production, Condé Nast invests heavily in companion products to improve and enhance our audience's experience. One such product solution is Spire, Condé Nast’s service for user segmentation and targeted advertising for over a hundred million users.

While Spire started as a set of databricks notebooks, we later utilized DBFS for deploying Spire distributions in the form of Python Whls, and more recently, we have packaged the entire production environment into docker images deployed onto our Databricks clusters. In this talk, we will walk through the process of evolving our python distributions and production environment into docker images, and discuss where this has streamlined our deployment workflow, where there were growing pains, and how to deal with them.

In this session watch:
Harin Sanghirun, Machine Learning Engineer, Condé Nast
Max Cantor, Software Engineer, Condé Nast