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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

A simple project using the CLI


We are now ready to execute a simple Amazon ML workflow using the CLI. This includes the following:

  • Uploading files on S3
  • Creating a datasource and the recipe
  • Creating a model
  • Creating an evaluation
  • Prediction batch and real time

Let's start by uploading the training and validation files to S3. In the following lines, replace the bucket name aml.packt with your own bucket name.

To upload the files to the S3 location s3://aml.packt/data/ch8/, run the following command lines:

$ aws s3 cp ./ames_housing_training.csv s3://aml.packt/data/ch8/
upload: ./ames_housing_training.csv to s3://aml.packt/data/ch8/ames_housing_training.csv

$ aws s3 cp ./ames_housing_validate.csv s3://aml.packt/data/ch8/
upload: ./ames_housing_validate.csv to s3://aml.packt/data/ch8/ames_housing_validate.csv

An overview of Amazon ML CLI commands

That's it for the S3 part. Now let's explore the CLI for Amazon's machine learning service. All Amazon ML CLI commands are available at http://docs.aws.amazon...

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