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. This is the second course of the Advanced Machine Learning on Google Cloud series. After completing this course, enroll in the Image Understanding with TensorFlow on Google Cloud course.
Introduction
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Bewertungen
4.6 (931Â Bewertungen)
- 5 stars70,24Â %
- 4 stars21,69Â %
- 3 stars5,80Â %
- 2 stars1,50Â %
- 1 star0,75Â %
AK
6. Dez. 2018
It is very good course, gives good overview over large ML systems on cloud, a lot of examples from real implementations gives good understunding about problematics in projects realisations
AF
6. Mai 2019
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
Aus der Unterrichtseinheit
Designing High-Performance ML Systems
In this module, you identify performance considerations for machine learning models. Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
Unterrichtet von
Google Cloud Training
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