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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
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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

Summary

In this chapter, we leveraged the power of the AWS ecosystem to build a real-time streaming data classification pipeline. Our pipeline was able to classify streaming tweets using an Amazon ML classification endpoint.  The AWS data ecosystem is diverse and complex, and for a given problem, there are often several possible solutions and architectures. The Kinesis-Lambda-Redshift-Machine Learning architecture we built is simple, yet very powerful. 

The true strength of the Amazon ML service lies in its ease of use and simplicity. Training and evaluating a model from scratch can be done in a few minutes with a few clicks, and it can result in very good performances. Using the AWS CLI and the SDK, more complex data flows and model explorations can easily be implemented. The service is agile enough to become a part of a wider data flow by providing real-time classification and...

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