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.
Specialization: Advanced Machine Learning on Google Cloud
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Bewertungen
4.6 (931Â Bewertungen)
- 5 stars70,24Â %
- 4 stars21,69Â %
- 3 stars5,80Â %
- 2 stars1,50Â %
- 1 star0,75Â %
AJ
16. Mai 2021
Excellent overview of designing real-world ML systems. Some of the labs are daunting, but the emphasis is showing you what can be achieved, rather than achieving mastery within the course.
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
Introduction to Advanced Machine Learning on Google Cloud
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
Unterrichtet von
Google Cloud Training
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