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Course Description

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.
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Enroll Now - Select a section to enroll in

Section Title
Production Machine Learning Systems
Language of Delivery
English
Section Schedule
Date and Time TBA
Course Fee(s)
Course Fee non-credit $49.00
Drop Request Deadline
TBD
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