In this course, we dive into the components and best practices of building high-performing ML systems in production environments. We cover some of the most common considerations behind building these systems, e.g. static training, dynamic training, static inference, dynamic inference, distributed TensorFlow, and TPUs. This course is devoted to exploring the characteristics that make for a good ML system beyond its ability to make good predictions.
Introduction
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Reviews
4.6 (1,004 ratings)
- 5 stars70.91%
- 4 stars21.31%
- 3 stars5.57%
- 2 stars1.49%
- 1 star0.69%
AK
Dec 7, 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
May 7, 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
From the lesson
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.
Taught By
Google Cloud Training