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Learn Amazon SageMaker

You're reading from   Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781801817950
Length 554 pages
Edition 2nd Edition
Languages
Tools
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Author (1):
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Julien Simon Julien Simon
Author Profile Icon Julien Simon
Julien Simon
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Introduction to Amazon SageMaker
2. Chapter 1: Introducing Amazon SageMaker FREE CHAPTER 3. Chapter 2: Handling Data Preparation Techniques 4. Section 2: Building and Training Models
5. Chapter 3: AutoML with Amazon SageMaker Autopilot 6. Chapter 4: Training Machine Learning Models 7. Chapter 5: Training CV Models 8. Chapter 6: Training Natural Language Processing Models 9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks 10. Chapter 8: Using Your Algorithms and Code 11. Section 3: Diving Deeper into Training
12. Chapter 9: Scaling Your Training Jobs 13. Chapter 10: Advanced Training Techniques 14. Section 4: Managing Models in Production
15. Chapter 11: Deploying Machine Learning Models 16. Chapter 12: Automating Machine Learning Workflows 17. Chapter 13: Optimizing Prediction Cost and Performance 18. Other Books You May Enjoy

Using other storage services

So far, we've used S3 to store training data. At a large scale, throughput and latency can become a bottleneck, making it necessary to consider other storage services:

Working with SageMaker and Amazon EFS

EFS is a managed storage service compatible with NFS v4. It lets you create volumes that can be attached to EC2 instances and SageMaker instances. This is a convenient way to share data, and you can use it to scale I/O for large training jobs.

By default, files are stored in the Standard class...

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