Moving the heavy lifting of machine learning to the cloud is a great way to get large speed-ups. TensorFlow is an increasingly popular open source machine intelligence library that is especially well suited for deep learning. The Google Cloud Machine Learning Platform (Cloud ML) lets you do distributed training and serving of your TensorFlow models at scale.
Yufeng Guo walks you through this process in detail so that you’ll be ready to scale your own training and prediction services. Yufeng starts with an introduction to TensorFlow concepts and then explains how to use Cloud ML to do distributed training and scalable serving of your trained models. Join in to learn the design options for scaling up your machine learning as well as their trade-offs.
Yufeng Guo is a developer advocate for the Google Cloud Platform, where he is trying to make machine learning more understandable and usable for all. He enjoys hearing about new and interesting applications of machine learning, so be sure to share your use case with him.
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