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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
Languages
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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

Understanding how SageMaker invokes your code

When we worked with built-in algorithms and frameworks, we didn't pay much attention to how SageMaker actually invoked the training and deployment code. After all, that's what "built-in" means: grab what you need off the shelf and get to work.

Of course, things are different if we want to use our own custom code and containers. We need to understand how they interface with SageMaker so that we implement them exactly right.

In this section, we'll discuss this interface in detail. Let's start with the file layout.

Understanding the file layout inside a SageMaker container

To make our life simpler, SageMaker estimators automatically copy hyperparameters and input data inside training containers. Likewise, they automatically copy the trained model from the container to S3. At deployment time, they do the reverse operation, copying the model from S3 into the container.

As you can imagine, this requires...

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