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
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