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Effective Amazon Machine Learning

You're reading from Effective Amazon Machine Learning

Product type Book

Published in Apr 2017

Publisher Packt

ISBN-13 9781785883231

Pages 306 pages

Edition 1st Edition

Languages

Concepts

Machine Learning

Author (1):

Alexis Perrier

Table of Contents (17) Chapters

Title Page

Credits

About the Author

About the Reviewer

www.PacktPub.com

Customer Feedback

Dedication

Preface

1. Introduction to Machine Learning and Predictive Analytics

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

Choosing between RDS and Redshift

AWS offers no less than six different cloud database and SQL/NoSQL services: RDS, Aurora, DynamoDB, Redshift, Athena, and AWS Database Migration Service! Out of all these services, only two are compatible with Amazon Machine Learning: RDS and Redshift. You can store data in either service and create datasources from these sources. The datasource creation methods for the two services have similar parameters, but differ quite significantly when it comes to the underlying AWS service communication.

RDS and Redshift are very different services. Redshift is a data warehouse used to answer a few complex and long running queries on large datasets, while RDS is made for frequent, small, and fast queries. Redshift is more suited for massive parallel processing to perform operations on millions of rows of data with minimal latency, while RDS offers a server instance that runs a given database. RDS offers several different database types – MySQL, PostgreSQL, MariaDB...

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Authors (1)

Alexis Perrier

Alexis Perrier is a data science consultant with experience in signal processing and stochastic algorithms. He holds a master's in mathematics from Universit Pierre et Marie Curie Paris VI and a PhD in signal processing from Tlcom ParisTech. He is actively involved in the DC data science community. He is also an avid book lover and proud owner of a real chalk blackboard, where he regularly shares his fascination of mathematical equations with his kids.

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