Bring your laptop to this session! Azure Machine Learning Studio simplifies machine learning experimentation. In this hands-on tutorial, we will go through the end-to-end process of building, evaluating, fine-tuning and deploying a scalable predictive modelling web service using Azure Machine Learning Studio. By the end of the tutorial, attendees will have a deployed predictive modeling UI of their own, similar to one of these: http://demos.datasciencedojo.com/. All attendees will go through the following hands-on exercises:
• Exploring, visualizing and cleaning a dataset • Building and fine-tuning a predictive model • Evaluating and comparing predictive models • Deploying a predictive model as a full managed, scalable web service • Exposing the deployed predictive model as a web UI.
Adult Retrieval Victoria (ARV) coordinates major trauma situations for Ambulance Victoria, but the vital service was in need of some help of its own in the form of better decision support to manage cases. They were transitioning from paper to online processes—the interim system they were using did not manage post-case administration digitally. ARV teamed up with software company Readify to develop groundbreaking technology which continues to transform the treatment of critically ill patients across the state of Victoria in Australia.
La realizzazione della Private Cloud richiede automazione, la creazione di nuovi sistemi, il deployment di nuove applicazioni, l'allocazione di maggiori risorse, tutto deve essere fatto meglio e prima. System Center Virtual Machine Manager permette di gestire il deployment della propria Private Cloud dalla fabric agli applicativi. In questa sessione entreremo nei meccanismi e nelle automazioni che Virtual Machine Manager mette a disposizione per realizzare questi obiettivi.