Twitter is a large company with many ML use cases. Historically, there have been many ways to productionize ML at Twitter—TensorFlow, Lua Torch, PyTorch, scikit-learn, VW, XGBoost, and several other Twitter in-house solutions. Yi Zhuang and Nicholas Leonard describe the setup and benefits of a unified ML platform for production and explain how the Twitter Cortex team brings together users of various ML tools.
Yi Zhuang is a machine learning software engineer at Twitter Cortex, where he tech leads a group of people to build a platform for working with ML models. Currently, he works on uniting ML practitioners around a single ML platform, bringing consistency to ML practices at Twitter. 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 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. He graduated from the Royal Military College of Canada and holds an MS in computer science from the University of Montreal.
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