Cibele Montez Halasz
Cibele is a Senior 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.
Optimizing Training for Sparse Workloads in Tensorflow
Twitter is a company with massive amounts of data. Thus, it is no wonder that the company applies machine learning in myriad of ways. In this session, 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. 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.