Twitter is a 4000+ employee company with many ML use cases. Historically, there are many different ways to productionize ML at Twitter. Across the company, we had users of TensorFlow, Lua Torch, PyTorch, Scikit Learn, VW, XGBoost, and several other Twitter in-house solutions. In this session, we are going to discuss:
Yi Zhuang is a software engineer at Twitter, where he tech leads a group of people to build platform and infrastructure for working with ML models. Currently, he works on unifying Twitter around a single ML framework & infrastructure, bringing together users of Lua Torch, PyTorch, Scikit Learn, XGBoost, VW, and other in house ML solutions. Previously, Yi led a group of people to develop a trillion-document scale distributed search engine at Twitter. Yi holds an MS in computer science from Carnegie Mellon University. He loves cats and enjoys pondering over all things technical and logical.
Nicholas Leonard graduated from the Royal Military College of Canada in 2008. He obtained an MS in computer science from University of Montreal in 2014. Nicholas is a software engineer at Twitter Cortex. He was a core contributor to Lua Torch, and currently works with TensorFlow as part of the DeepBird team.
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