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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 Aug 2020
Publisher Packt
ISBN-13 9781800208919
Length 490 pages
Edition 1st Edition
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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: Introduction to 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 Computer Vision 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 on 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

Deploying models to container services

Previously, we saw how to fetch a model artifact in S3 and how to extract the actual model from it. Knowing this, it's pretty easy to deploy it on a container service, such as Amazon ECS, Amazon EKS, or Amazon Fargate.

Maybe it's company policy to deploy everything in containers, maybe you just like them, or maybe both! Whatever the reason is, you can definitely do it. There's nothing specific to SageMaker here, and the AWS documentation for these services will tell you everything you need to know.

A sample high-level process could look like this:

  1. Train a model on SageMaker.
  2. When training is complete, grab the artifact and extract the model.
  3. Push the model to a Git repository.
  4. Write a task definition (for ECS and Fargate) or a pod definition (for EKS). It could use one of the built-in containers or your own. Then, it could run a model server or your own code to clone the model from your Git repository...
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