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

View More author details

Table of Contents (19) Chapters

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

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Processing data with other AWS services

Over the years, AWS has built many analytics services (https://aws.amazon.com/big-data/). Depending on your technical environment, you could pick one or the other to process data for your machine learning workflows.

In this section, you'll learn about three services that are popular choices for analytics workloads, why they make sense in a machine learning context, and how to get started with them:

Amazon Elastic Map Reduce (EMR)
AWS Glue
Amazon Athena

Amazon Elastic Map Reduce

Launched in 2009, Amazon Elastic Map Reduce, aka Amazon EMR, started as a managed environment for Apache Hadoop applications (https://aws.amazon.com/emr/). Over the years, the service has added support for plenty of additional projects, such as Spark, Hive, HBase, Flink, and more. With additional features like EMRFS, an implementation of HDFS backed by Amazon S3, EMR is a prime contender for data processing at scale. You can learn...