TFX: An end-to-end ML platform for everyone
TensorFlow Extended (TFX) has evolved as the ML platform solution within Alphabet over the past decade, and Alphabet is now evangelizing TFX for the rest of the world. Konstantinos Katsiapis and Anusha Ramesh discuss what it means to be an “ML platform” and share Alphabet’s insights and approach that laid the foundation for TFX to reach its current popularity within Alphabet—and which the company hopes to amplify beyond Alphabet.
Konstantinos (Gus) Katsiapis is the über tech lead of TensorFlow Extended (TFX), an end-to-end machine learning platform based on TensorFlow. He’s worked on Sibyl, a massive-scale machine learning system (precursor to TensorFlow) widely used at Google, and was an avid user of machine learning infrastructure while leading the mobile display ads quality machine learning team at Google. Previously, Gus gathered knowledge and experience at Amazon, Calian, the Ontario Ministry of Finance, Independent Electricity System Operator, and Computron. He holds a master’s degree in computer science with a specialization in artificial intelligence from Stanford University and a bachelor’s degree in mathematics, majoring in computer science and minoring in economics, from the University of Waterloo.
Anusha Ramesh is a product manager for TensorFlow at Google Brain. She works on TensorFlow Extended, which a production-scale machine learning platform. Previously, Anusha was a product lead at a fashion tech startup that builds personalized recommendations for women’s fashion. She has a master’s degree in information networking from Carnegie Mellon.
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