| Summary: | "Machine learning has allowed Twitter to drive engagement, promote healthier conversations, and deliver catered advertisements. Cibele Montez Halasz and Satanjeev Banerjee describe one of those use cases: timeline ranking. They share some of the optimizations that the team has made--from modeling to infrastructure--in order to have models that are both expressive and efficient. You'll explore the feature pipeline, modeling decisions, platform improvements, hyperparameter tuning, and architecture (alongside discretization and isotonic calibration) as well as some of the challenges Twitter faced by working with heavily text-based (sparse) data and some of the improvements the team made in its TensorFlow-based platform to deal with these use cases. Join in to gain a holistic view of one of Twitter's most prominent machine learning use cases."--Resource description page
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