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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

Making batch predictions


Making batch predictions on Amazon ML is straightforward and follows this process:

  1. From the dashboard, create a new Batch prediction.
  2. Select the model.
  3. Select the datasource on which to apply the model.
  4. Set the prediction output folder and grant permissions.
  5. Review and launch.

We call the prediction dataset or datasource, the data on which we want to make predictions. In this chapter, we are in a testing context and the prediction dataset is the held-out dataset we extracted from the whole original dataset. In a real-world context, the prediction dataset refers to entirely new data and does not include the target variable. 

The prediction can only work if the distribution of the prediction dataset is similar to the distribution of the training dataset on which the model has been trained. The prediction datasource and the training datasource must also share the same schema, with one difference the prediction dataset does not need to include the target variable. Amazon ML...

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