Reactive Machine Learning and Functional Programming

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The document discusses reactive machine learning and functional programming. It describes how reactive systems are responsive, resilient, elastic and message-driven. It then provides examples of how data collection events and functional transformations can be used in a reactive machine learning pipeline. Models are treated as pure functions and supervised through a model supervisor architecture. The document concludes by recommending several reactive resources and frameworks that can enable large-scale reactive machine learning.

Software

Reactive Machine Learning &
Functional Programming

Responsive
Resilient Elastic
Message-Driven

Replication
Isolation/
Containment
Supervision/
Delegation

Lapdog
Model
French
Bulldog
Model
Model Supervisor

Lapdog
Model
French
Bulldog
Model
French
Bulldog
Model
Model Supervisor

Lapdog
Model
French
Bulldog
Model
French
Bulldog
Model
Model Supervisor
No one
likes dogs!

Lapdog
Model
French
Bulldog
Model
Model Supervisor
No one
likes dogs!

reactivemachinelearning.com
@jeffksmithjr

Resources
• Reactive Persistence Interaction
• http://reactivemongo.org/
• http://websudos.github.io/phantom/
• https://github.com/softwaremill/reactive-kafka

Resources
• Spark Pipelines
• https://github.com/intentmedia/mario
• http://blogs.aws.amazon.com/bigdata/post/
Tx21LOP0UQ2ZA9N/Large-Scale-Machine-
Learning-with-Spark-on-Amazon-EMR
• Reactive Microservices
• https://www.typesafe.com/activator/template/akka-
http-microservice

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