Reactive Machine Learning & Functional Programming • Jeffrey Smith • YOW! 2015

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This presentation was recorded at YOW! 2015. #GOTOcon #YOW https://yowcon.com Jeffrey Smith - Data Engineer at Intent Media ABSTRACT The reactive manifesto has sparked off a ton of excitement around building modern distributed systems using functional programming principles. But how can the reactive principles be applied to the most exciting app in all of big data: machine learning? In this talk, I’ll walk through in detail how to build machine learning applications that are responsive, resilient, elastic, and message-driven. We’ll see how reactive machine learning system build on important concepts such as futures, actors, and supervision but require modern implementations such as Scala futures, Akka, and Spark. Building a reactive machine learning system is ultimately an architectural problem, and this talk will discuss the relationship between reactive systems architectures and other important architectural patterns such as the Lambda Architecture, Event Sourcing, and CQRS. Examples will be shown in Scala using Spark with a focus on how functional programming idioms enable the implementation of reactive machine learning systems. [...] Christiansen, Foy, Wall & Orwant • Programming Perl • https://amzn.to/3DJAiKk Gamma, Helm, Johnson & Booch • Design Patterns (Gang of Four) • https://amzn.to/3sPJ8yk Lee, Wickens, Liu & Boyle • Designing for People • https://amzn.to/313ovpi Phil Winder • Reinforcement Learning • https://amzn.to/3t1S1VZ Holden Karau, Trevor Grant, Boris L

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