This course covers how to implement the various flavors of production ML systems— static, dynamic, and continuous training; static and dynamic inference; and batch and online processing. You delve into TensorFlow abstraction levels, the various options for doing distributed training, and how to write distributed training models with custom estimators.
이 강좌에 대하여
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PRODUCTION MACHINE LEARNING SYSTEMS의 최상위 리뷰
Course is a bit outdated. E.g., KubeFlow section and references to ML Engine.
Very practical which was nice. Thank you for adding the Quicklabs that helped a lot.
It was bit hard course but lab work was great and learn many production level consideration for ml systems.
Really focuses on topics for building production ML Systems
자주 묻는 질문
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Last Updated: September 1, 2015
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