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.
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- 5 stars70.46%
- 4 stars21.38%
- 3 stars5.86%
- 2 stars1.52%
- 1 star0.76%
來自PRODUCTION MACHINE LEARNING SYSTEMS的熱門評論
I did not realize the many aspects to consider implementing a Production ML system. This course presents all of them and provides guidance for evaluating alternative
It was bit hard course but lab work was great and learn many production level consideration for ml systems.
very good information. Lot of unknown facts in ML are brought up in the course.
very good for people who will enter the production stage on machine learning systems
该课程是 Coursera 上提供的为数不多的课程之一，目前只对已购买课程或已获得助学金的学生开放。
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Last Updated: September 1, 2015