Attend this session to learn how to leverage the scale and infrastructure of Azure to deploy large sets of Linux VMs in the cloud. If you are running a large Linux server farm and are wondering what steps it takes to move it to Azure, this session will provide you with guidance and best practices. You will see how to script and simplify this effort as well as learn key considerations and principles. We will also walk through best practices for configuring and deploying the data tier of your workload.
The Lync Call Quality Methodology (CQM) is a practical approach that applies the broad guidance we have published in the Lync Networking Guide. CQM is a data driven framework backed by SQL queries and an operational process to systematically improve and maintain call quality. CQM was derived from Microsoft and some of our largest customer deployments. CQM was published in v2 of the Networking Guide complete with queries and an updated set of 2010/2013 KHIs. This session will go through the individual components of CQM, describe our recommended process for implementing it, show findings from using CQM and go into detail on how the SQL queries work and how you can customize them for you or your customer's needs.
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.