GOTO is a vendor independent international software development conference with more that 90 top speaker and 1300 attendees. The conference cover topics such as .Net, Java, Open Source, Agile, Architecture and Design, Web, Cloud, New Languages and Processes

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Oscar Naim, Principal Program Manager at Microsoft Corporation

Oscar Naim

Biography: Oscar Naim

Oscar Naim is a Principal Program Manager at Microsoft Corporation (Information Management and Machine Learning group). He has more than 19 years of experience in the computing industry, including positions at Microsoft, Intel, Oracle and the University of Wisconsin-Madison. He has PhD in Computer Sciences from the University of Southampton, UK (with Tony Hey as his supervisor), as well as a Master and Bachelor’s degree in Computer Sciences from Universidad Simon Bolivar, Venezuela. Oscar is passionate about helping customers consume technologies in a way that adds value to their products and overall user experience.

In his free time, Oscar loves to play classical guitar, cook, and spend time with his wife and 3 very active boys.

Presentation: Azure Machine Learning. Machine Learning with the simplicity and productivity of the cloud

Track: Microsoft Technologies / Time: Monday 13:20 - 14:10 / Location: Kammersalen

Microsoft Azure Machine Learning (MAML) is a fully managed service on Windows Azure which a developer can use to build a predictive analytics model using machine learning over data and then deploy her model as a web service.  ML Studio is accessible through a web browser, with no software to purchase or install, and the authoring experience is through visual composition. There are modules in Azure ML to support the end-to-end data science workflow for constructing a predictive model, from ready access to common data sources, data exploration, feature selection and creation, building training and testing sets, machine learning over data, and final model evaluation and experimentation. In this talk I will present an overview of the basic data science workflow, with details on select machine learning algorithms, then build a predictive analytics model using real world data, evaluate several different machine learning algorithms, then deploy the finished model as a machine learning web service within minutes. I will close this talk with a forward look on machine learning and the integration of intelligent web services in everyday applications and services. Please, give Azure ML a try at http://azure.com/ml. The future of Machine Learning is here!“