Machine Learning has allowed Twitter to drive engagement, promote healthier conversations, and deliver catered advertisements.This session will focus on explaining, in depth, the use case of one of teams that benefits from our Machine Learning platform: Timelines Ranking. We plan to discuss its feature pipeline, modeling decisions as well as platform improvements. In the modeling side, we will be discussing hyperparameter tuning as well as different architecture explorations (alongside discretization and isotonic calibration). In the platform side, we will be exploring some of the challenges Twitter faced by working with heavily text-based (sparse) data and some of the improvements we have made in our Tensorflow-based platform to deal with these use cases. Overall, we plan to give a holistic view into one of Twitter’s most prominent use cases.
Cibele is a Machine Learning Engineer at Twitter Cortex, where she helps to build Twitter’s deep learning platform. Prior to working at Twitter, Cibele worked at Apple as a Data Scientist and Systems Design Engineer; and at Analog Devices as Product Applications Engineer . At Analog Devices, she worked on building machine learning algorithms that use smartphone sensors to understand a person’s behavior. Cibele obtained her B.S. from Stanford University in Electrical Engineering and Physics and her M.S. from the California Institute of Technology in Electrical Engineering with an emphasis in Computer Vision and Machine Learning.
Mishael is a Software Engineer at Twitter Timelines Quality team, focusing on relevance-based timelines ranking. Since 2011 Mishael has been utilizing Machine Learning to tackle real-world problems in various domains, such as hardware verification and chat agent allocation. He also has vast experience developing data science tools, pipelines and infrastructure. Mishael obtained his BSc in Mathematics and Computer Science and MSc in Theoretical Computer Science from the Hebrew University of Jerusalem.
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