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Moving the heavy lifting of machine learning to the cloud is a great way to scale both training and prediction. This session will step through this process in detail, so that you'll be ready to scale your machine learning tasks.
TensorFlow is an increasingly popular open source Machine Intelligence library that is especially well-suited for deep learning. The Google Cloud Machine Learning Engine (Cloud ML Engine) lets you do distributed training and serving of your TensorFlow models at scale.
Join Yufeng as he kicks off with an introduction to TensorFlow concepts and then walks through how to use Cloud ML Engine to do distributed training and scalable serving of your trained models. You will explore the design decisions available to you in scaling up your machine learning, and talk about their tradeoffs.
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Machine Learning at scale: TensorFlow in the Cloud - Intermediate
Yufeng is a Developer Advocate focusing on Cloud AI, where he is working to make machine learning more understandable and usable for all. He is the creator of the YouTube series AI Adventures, at yt.be/AIAdventures, exploring the art, science, and tools of machine learning.