Book Image

Effective Amazon Machine Learning

By : Alexis Perrier
Book Image

Effective Amazon Machine Learning

By: Alexis Perrier

Overview of this book

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

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...