Production Machine Learning Systems
This course is part of multiple programs. Learn more
Instructor: Google Cloud Training
What you'll learn
Skills you'll gain
There are 6 modules in this course
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.
Architecting Production ML Systems
Designing Adaptable ML Systems
Designing High-Performance ML Systems
Building Hybrid ML Systems
Summary
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