Production Machine Learning Systems

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.

• Compare static vs. dynamic training and inference

• Manage model dependencies

• Set up distributed training for fault tolerance, replication, and more

• Export models for portability

Syllabus

Syllabus - What you will learn from this course
Week 1
Introduction to Advanced Machine Learning on Google Cloud
Architecting Production ML Systems
Week 2
Designing Adaptable ML Systems
Designing High-Performance ML Systems
Week 3
Building Hybrid ML Systems
Summary

FAQ

Can I preview a course before enrolling?

Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.

What will I get when I enroll?

Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.

When will I receive my Course Certificate?

If you complete the course successfully, your electronic Course Certificate will be added to your Accomplishments page - from there, you can print your Course Certificate or add it to your LinkedIn profile.

Why can’t I audit this course?

This course is one of a few offered on Coursera that are currently available only to learners who have paid or received financial aid, when available.

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Last Updated: September 1, 2015

Reviews

Course is a bit outdated. E.g., KubeFlow section and references to ML Engine.

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

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

very good for people who will enter the production stage on machine learning systems