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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Production Machine Learning Systems
This course is part of multiple programs.
Instructor: Google Cloud Training
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(994 reviews)
What you'll learn
Compare static versus dynamic training and inference
Manage model dependencies
Set up distributed training for fault tolerance, replication, and more
Export models for portability
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There are 6 modules in this course
This module previews the topics covered in the course and how to use Qwiklabs to complete each of your labs using Google Cloud.
What's included
2 videos2 readings
This module explores what else a production ML system needs to do and how to meet those needs. You review how to make important, high-level, design decisions around training and model serving need to make in order to get the right performance profile for your model.
What's included
10 videos1 reading1 assignment1 app item
In this module, you learn how to recognize the ways that our model is dependent on our data, make cost-conscious engineering decisions, know when to roll back our models to earlier versions, debug the causes of observed model behavior and implement a pipeline that is immune to one type of dependency.
What's included
14 videos1 reading1 assignment3 app items
In this module, you identify performance considerations for machine learning models. Machine learning models are not all identical. For some models, you focus on improving I/O performance, and on others, you focus on squeezing out more computational speed.
What's included
14 videos1 reading1 assignment2 app items
Understand the tools and systems available and when to leverage hybrid machine learning models.
What's included
7 videos1 reading1 assignment1 app item
This module reviews what you learned in this course.
What's included
1 video2 readings
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DeepLearning.AI
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Last Updated: September 1, 2015
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.
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