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.
Are you interested in the future and the roadmap for the WPF platform? If so, this session is for you. We will take an in-depth look at our areas of investment and the new features we are working on for .NET 4.6 and future releases of the WPF Platform. We will talk about how WPF platform will evolve in the long term, and the strategies you should start considering to get the best out of future platform improvements. Finally, we will take a look at the new authoring, debugging and diagnostics experiences available for WPF applications in Visual Studio 2015.