Production Machine Learning Systems
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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 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.
Author Name: Google Cloud
Author Description:
Google Cloud can help solve your toughest problems and grow your business. With Google Cloud, their infrastructure is your infrastructure. Their tools are your tools. And their innovations are your innovations.
Table of Contents
- Introduction to Advanced Machine Learning on Google Cloud
3mins - Introduction to Advanced Machine Learning on Google Cloud
3mins - Architecting Production ML Systems
39mins - Architecting Production ML Systems
35mins - Designing Adaptable ML Systems
45mins - Designing Adaptable ML Systems
45mins - Designing High-Performance ML Systems
42mins - Designing High-Performance ML Systems
42mins - Building Hybrid ML Systems
20mins - Building Hybrid ML Systems
20mins - Summary
1min - Summary
1min - Course Resources
0mins - Course Resources
0mins
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