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.
Dieser Vortrag zeigt nicht den "üblichen" RSS Reader, sondern stellt die verschiedenen APIs der Windows-Plattform in einer Demo der etwas anderen Art vor. Geboten wird jede Menge an Inspiration und Ideen für App-Entwickler und ihre kommenden Projekte.
Speaker: Dariusz Parys - Blog | Twitter, Gunter Logemann