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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

Introduction to Machine Learning and Predictive Analytics

As artificial intelligence and big data have become a ubiquitous part of our everyday lives, cloud-based machine learning services are part of a rising billion-dollar industry. Among the several services currently available on the market, Amazon Machine Learning stands out for its simplicity. Amazon Machine Learning was launched in April 2015 with a clear goal of lowering the barrier to predictive analytics by offering a service accessible to companies without the need for highly skilled technical resources.

This introductory chapter is a general presentation of the Amazon Machine Learning service and the types of predictive analytics problems it can solve. The Amazon Machine Learning platform distinguishes itself by its simplicity and straightforwardness. However, simplicity often implies that hard choices have been made. We explain what was sacrificed, why these choices make sense, and how the resulting simplicity can be extended with other services in the rich data-focused AWS ecosystem.

We explore what types of predictive analytics projects the Amazon Machine Learning platform can address and how it uses a simple linear model for regression and classification problems. Before starting a predictive analytics project, it is important to understand what context is appropriate and what constitutes good results. We present the context for successful predictions with Amazon Machine Learning (Amazon ML).

The reader will understand what sort of problems Amazon ML can address and the assumptions with regard to the underlying data. We show how Amazon ML solves linear regression and classification problems with a simple linear model and why that makes sense. Finally, we present the limitations of the platform.

This chapter addresses the following topics:

  • What is Machine Learning as a Service (MLaaS) and why does it matter?
  • How Amazon ML successfully leverages linear regression, a simple and powerful model
  • What is predictive analytics and what types of regression and classification problems can it address?
  • The necessary conditions the data must verify to obtain reliable predictions
  • What's missing from the Amazon ML service?
You have been reading a chapter from
Effective Amazon Machine Learning
Published in: Apr 2017
Publisher: Packt
ISBN-13: 9781785883231
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