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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

Discovering the built-in frameworks in Amazon SageMaker

SageMaker lets you train and deploy your models with all major machine learning and deep learning frameworks:

  • Scikit-Learn is undoubtedly the most widely used open source library for machine learning. If you're new to this topic, start here: https://scikit-learn.org.
  • XGBoost is an extremely popular and versatile open source algorithm for regression, classification, and ranking problems (https://xgboost.ai). It's also available as a built-in algorithm, as presented in Chapter 4, Training Machine Learning Models. Using it in framework mode will give us more flexibility.
  • TensorFlow is the #1 open source library for deep learning (https://www.tensorflow.org). SageMaker supports both the 1.x and 2.x versions, as well as the lovable Keras API (https://keras.io).
  • PyTorch is another highly popular open source library for deep learning (https://pytorch.org). Researchers in particular enjoy its flexibility...
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