10
Production Machine Learning Systems
10
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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After finishing this course, you can explore additional content in your learning path or browse the catalog.
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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.
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View the public classroom schedule here.
Can I take this course for free?
When you enroll into most courses, you will be able to consume course materials like videos and documents for free. If a course consists of labs, you will need to purchase an individual subscription or credits to be able consume the labs. Labs can also be unlocked by any campaigns you participate in. All required activities in a course must be completed to be awarded the completion badge.