In this mobile-first and cloud first world we take you on a journey, where you, the audience, participate in demonstrating what is possible when building a Universal application linked to the cloud. The demo walks through some of the basic data binding features which some of you know and love, to a grand finale of scenarios using Mobile Services and SignalR. This session requires audience participation through the use of your mobile devices.
Graeme Hackland joined the Williams Group in January 2014 as IT Director, 2016 is Graeme's 20th season in Formula 1. Graeme serves on the company's Executive Committee and leads the IT Risk Committee. Williams Group are currently undergoing a business improvement program and brought Graeme on board to drive the technology program.
Graeme is a Fellow of BCS, a member of the IEEE and IEEE Computer Society and holds a National Diploma in Electronic Engineering (LC) from Natal Technikon, South Africa. Outside of work Graeme is a husband, father, and ultra-marathon runner.
Esta sesión no apta para principiantes pretende mostrarte los mejores trucos para el desarrollo y depuración de servicios y aplicaciones basadas en Azure. Después de algo más de cuatro años trabajando con Azure Cloud Services y otros pocos sobre Azure Websites nos hemos topado con todo tipo de situaciones en las que las herramientas de trabajo comunes se nos han quedado cortas. Ven y aprende técnicas avanzadas de depuración y detección de bugs en Azure Cloud Services y Azure Websites usando WireShark, WinDbg y Visual Studio Ultimate entre otros, así como los mejores trucos para startup tasks, remote PowerShell y el anillo único de poder.
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.