Table of Contents

 

Copyright

Brief Table of Contents

Table of Contents

Foreword

Preface

Acknowledgments

About this book

About the author

About the cover illustration

1. Fundamentals of reactive machine learning

Chapter 1. Learning reactive machine learning

1.1. An example machine learning system

1.1.1. Building a prototype system

1.1.2. Building a better system

1.2. Reactive machine learning

1.2.1. Machine learning

1.2.2. Reactive systems

1.2.3. Making machine learning systems reactive

1.2.4. When not to use reactive machine learning

Summary

Chapter 2. Using reactive tools

2.1. Scala, a reactive language

2.1.1. Reacting to uncertainty in Scala

2.1.2. The uncertainty of time

2.2. Akka, a reactive toolkit

2.2.1. The actor model

2.2.2. Ensuring resilience with Akka

2.3. Spark, a reactive big data framework

Summary

2. Building a reactive machine learning system

Chapter 3. Collecting data

3.1. Sensing uncertain data

3.2. Collecting data at scale

3.2.1. Maintaining state in a distributed system

3.2.2. Understanding data collection