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.
Introduction
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Bewertungen
4.6 (909 Bewertungen)
- 5 stars70,29 %
- 4 stars21,56 %
- 3 stars5,83 %
- 2 stars1,54 %
- 1 star0,77 %
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
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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