The authors present basic concepts about Spring Boot and Netflix OSS software and how to integrate Netflix OSS technologies into Spring Boot.
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
Ruslan Meshenberg discusses Netflix's challenges, operational tools and best practices needed to provide high availability through multiple regions.
Dianne Marsh describes how Netflix' tooling, especially the continuous delivery system, allows developers to push the button for production deployment, and helps them to recover if necessary.
In this talk Jafar Husain and Matthew Podwysocki explore the Reactive Extensions (Rx) library which allows to treat events as collections. Also: how Netflix uses Rx on the client and the server.
Roy Rapoport discusses canary analysis deployment and observability patterns he believes that are generally useful, and talks about the difference between manual and automated canary analysis.
Carl Quinn explains how Riot Games built a cloud platform based on the Netflix OSS stack plus a number of other extensions including Dropwizard, Eureka, Archaius, Asgard, Edda, etc.
Jafar Husain, Matthew Podwysocki teach developers to think about events as collections, demonstrating some basic collection operations to express complex asynchronous programs as simple expressions.
Jafar Husain explains how Netflix uses reactive programming to build and consume REST endpoints, and how they work around the limitations of the HTTP protocol to create high-performance REST APIs.
Sudhir Tonse presents Netflix' composable PaaS built with several components that have been open sourced.
Xavier Amatriain discusses the machine learning algorithms and architecture behind Netflix' recommender systems, offline experiments and online A/B testing.
Dianne Marsh presents the open source tools used by Netflix to keep the continuous delivery wheels spinning.