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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

Discovering Amazon SageMaker Autopilot

Added to Amazon SageMaker in late 2019, Amazon SageMaker Autopilot is an AutoML capability that takes care of all machine learning steps for you. You only need to upload a columnar dataset to an Amazon S3 bucket, and define the column you want the model to learn (the target attribute). Then, you simply launch an Autopilot job, with either a few clicks in the SageMaker Studio GUI, or a couple of lines of code with the SageMaker SDK.

The simplicity of SageMaker Autopilot doesn't come at the expense of transparency and control. You can see how your models are built, and you can keep experimenting to refine results. In that respect, SageMaker Autopilot should appeal to new and seasoned practitioners alike.

In this section, you'll learn about the different steps of a SageMaker Autopilot job, and how they contribute to delivering high-quality models:

  • Analyzing data
  • Feature engineering
  • Model tuning

Let&apos...

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