Production Machine Learning Systems
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 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.
Course Info
Objectives
- Compare static versus dynamic training and inference
- Manage model dependencies
- Set up distributed training for fault tolerance, replication, and more
- Export models for portability
Prerequisites
Basic SQL, familiarity with Python and TensorFlow
Audience
Data Engineers and programmers interested in learning how to apply machine learning in practice.
Anyone interested in learning how to leverage machine learning in their enterprise.
Available languages
English, español (Latinoamérica), français, 日本語, and português (Brasil)
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Interested in taking this course with one of our authorized on-demand partners?
Explore Google Cloud content on Coursera and Pluralsight.
Prefer learning with an instructor?
View the public classroom schedule here.
Can I take this course for free?
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