As machine learning evolves from experimentation to serving production workloads, so does the need to effectively manage the end-to-end training and serving workflow, including model sharing (TF Hub), analysis (TF Model Analysis), and serving (TF Serving). Kenny Song and Quentin de Laroussilhe offer an overview of TensorFlow Extended, the end-to-end machine learning platform for TensorFlow that powers products across all of Google.
This session is sponsored by Google.
Kenny Song is a product manager on the Google Brain team working on TensorFlow Extended, an end-to-end production ML platform used across Google. He focuses on making ML systems easier to build, deploy, and understand. Previously, Kenny served in software engineering and product management roles at Jigsaw (an Alphabet company), Project Loon, and Google Shopping. He holds a degree in mathematics and computer science from New York University Shanghai.
Quentin de Laroussilhe is a Zurich-based software engineer on the applied machine intelligence team at Google AI, where he is helping various product areas deploy machine learning solutions for their use cases. He focuses on research topics including transfer learning, black-box optimization, and reinforcement learning. Previously, Quentin worked in image and voice search and on the Google Assistant.
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