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Effective Amazon Machine Learning

You're reading from   Effective Amazon Machine Learning Expert web services for machine learning on cloud

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Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785883231
Length 306 pages
Edition 1st Edition
Languages
Tools
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Author (1):
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Alexis Perrier Alexis Perrier
Author Profile Icon Alexis Perrier
Alexis Perrier
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction to Machine Learning and Predictive Analytics FREE CHAPTER 2. Machine Learning Definitions and Concepts 3. Overview of an Amazon Machine Learning Workflow 4. Loading and Preparing the Dataset 5. Model Creation 6. Predictions and Performances 7. Command Line and SDK 8. Creating Datasources from Redshift 9. Building a Streaming Data Analysis Pipeline

Boto3, the Python SDK


Another tool to interact with the Amazon ML service outside of the web interface is an SDK. Simply put, an SDK is a wrapper around an API that makes working with the service much simpler and more efficient, as many details of the interactions are taken care of. AWS offers SDKs in the most widespread languages such as PHP, Java, Ruby, .Net, and of course, Python. In this chapter, we will focus on working with the Amazon ML service through the Python SDK. The Python SDK requires the Boto3 module.

Installation of the Boto3 module is done via pip. Refer to the quickstart guide available at http://boto3.readthedocs.io/en/latest/guide/quickstart.html if you need more information and troubleshooting:

pip install boto3

Boto3 is available for most AWS services. The complete list can be found at http://boto3.readthedocs.io/en/latest/reference/services/index.html. We will focus on Boto3 for S3 and Amazon ML.

Setting up permissions for SDK access can be done via the aws configure command...

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