Testability is more important than ever. With short ship cycles and the desire for continuous delivery, it is critical to quickly know if a modification has destabilized your code base. This session will enable you to use a dependency injection container of your choice to create testable code. We will examine tightly coupled code and what problems it causes and how DI can be used to avoid these problems. The Unity DI container will be used as the medium to understand the concepts.
For machine learning, Hadoop offers new performance capabilities, but not the intrusion of Hadoop's accompanying tradeoffs--performance, resource consumption, and data management. Machine learning users should consider Hadoop as a portion of a solution, but not the end-all. Alternatives such as dedicated servers, in-database deployment, and memory-based alternatives like Apache Spark can be combined with Hadoop to address a far broader array of opportunities. Fortunately for Revolution R users, Revolution R Enterprise (RRE) enables analytical scripts and models built in RRE to port between platforms with relative ease. In this session, we'll review the considerations for R developers, including performance, resource management, and data handling for deployment on Hadoop, individual servers, clusters and grids, in-database, and in-memory, including Apache Spark. We'll also dive briefly into the internals RRE on Hadoop to deepen awareness of some of the tradeoffs.