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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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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

Getting started and setting up


Creating a performing predictive model from raw data requires many trials and errors, much back and forth. Creating new features, cleaning up data, and trying out new parameters for the model are needed to ensure the robustness of the model. There is a constant back and forth between the data, the models, and the evaluations. Scripting this workflow either via the AWS CLI or with the Boto3 Python library, will give us the ability to speed up the create, test, select loop.

Using the CLI versus SDK

AWS offers several ways besides the UI to interact with its services, the CLI, APIs, and SDKs in several languages. Though the AWS CLI and SDKs do not include all AWS services. Athena SQL, for instance, being a new service, is not yet included in the AWS CLI module or in any of AWS SDK at the time of writing.

The AWS Command Line Interface or CLI is a command-line shell program that allows you to manage your AWS services from your shell terminal. Once installed and set...

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