This course teaches how to apply reactive principles to machine learning applications, focusing on building systems that are responsive, resilient, elastic, and message-driven. Students will learn about important concepts such as futures, actors, and supervision, and will work with modern implementations like Scala futures, Akka, and Spark. The course emphasizes the relationship between reactive systems architectures and other architectural patterns, showcasing examples in Scala using Spark. The intended audience for this course includes data engineers, machine learning enthusiasts, and those interested in functional programming and reactive systems.
Reactive Machine Learning and Functional Programming
Overview
Syllabus
Introduction
Example Problem
Reactive Data Architecture
Feature Extraction
Model Learning
Model Implementation
Model Supervisor
Taught by
GOTO Conferences
