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

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800208919

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

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...