Amazon Machine Learning
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Amazon Machine Learning

An Introduction

About the Book

Would you like to perform machine learning without setting up any infrastructure? Amazon Machine Learning (AML) lets you do supervised learning against datasets in S3, RDS and Redshift and build models that can be accessed via realtime or batch predictions. In this book I'll cover how to get your data up to Amazon, how to build your model and how to use your model. We'll also cover areas where AML might not be a good fit, and how to access your model via the API.

About the Author

Dan Moore
Dan Moore

Dan Moore has been developing for the web since 1997, using all manner of (unixish) technologies, from perl to java to php to javascript to Cordova.

Recently he's been working more with the AWS technology stack, and is has been an instructor for AWS training programs.

Table of Contents

  • Introduction
  • What is Machine Learning
    • Common Issues
    • Why It Matters
  • What is Amazon Machine Learning
    • Key Elements
    • Interface
    • Regional Availability And Data Sources
    • Support
  • AML concepts
  • Types of Data Sources
    • S3
    • Redshift
    • RDS
    • External data
    • Conclusion
  • Loading Your Data
    • Real data
    • Prepping Your Data
    • IAM roles
    • Uploading Your Data To S3
    • Creating the Data Source
    • Statistics
    • Failure
  • Data Nuts and Bolts
    • Excluded Attributes
    • Known Data Types
    • Missing values
    • Data type differences
    • Row Identifiers
    • Statistics
  • Data considerations
    • How much data makes sense
    • Protecting your data
    • Server Side Encryption
    • Transformations Before You Create a Data Source
    • Anonymize your data
  • Initial Model Build
    • Building from the console
    • Custom Settings
    • Experiment
  • More About Your Model
    • Shuffling
    • Using recipes to modify the data your model sees
    • Transformations available
    • Example Recipe
    • Which recipe should you use
  • Batch vs real time predictions
    • How to decide
    • Batch
    • Real-time
    • Pick based on use case
  • More About Batch Predictions
    • Location of observations
    • Kicking off a batch prediction
    • How do you know when you’re done
    • Results
  • More About Real Time Predictions
  • Why Manage Your Models
    • Creating and Comparing Models With the Same Data Schema
    • Creating and Comparing Models With Different Schemas
    • Combining Model Results
  • Managing Via the API
  • Creating models via the API
  • Other Use Cases
  • Limits of AML
    • Read only models
    • Only structured text data
    • No model insight
    • Number of features
    • Category cardinality
    • Limited configurability
    • No model export
    • Server side encrypted data
    • Ill Fitting Use Cases
  • Alternatives
  • Conclusion

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