Part 1: Fundamentals of Reactive Machine Learning Systems
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. Traits of Reactive Machine Learning Systems
1.3. Summary
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
2.4. Summary
Part 2: Building a Reactive Machine Learning System
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
3.3. Persisting Data
3.3.1. Elastic and Resilient Databases
3.3.2. Fact Databases
3.3.3. Querying Persisted Facts
3.3.4. Understanding Distributed Fact Databases
3.4. Applications
3.5. Reactivities
3.6. Summary
4. Generating Features
5. Learning Models
6. Publishing Models
7. Predicting
Part 3: Operating a Reactive Machine Learning System
8. Delivering
9. Monitoring
10. Scaling
11. Evolving
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