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.
You've migrated parts of your application to in-memory. But now you are not seeing a serious performance improvement, or maybe even a performance degradation! What's going on? In this session we walk you through several performance problems we have seen with customers, and show how to diagnose and resolve the issues to ultimately obtain the performance improvement you're looking for from in-memory OLTP. We address a variety of issues, including log IO bottlenecks, index tuning, and query plan problems.
See highlights from the Jason Zander keynote at AzureCon, Infrastructure for the intelligent cloud.
Microsoft Azure is built on a hyper-scale, hybrid and trustworthy infrastructure platform that helps organizations add scale to their applications, be agile and enjoy cost savings. Learn how businesses are transforming themselves by evolving their IT strategy to take advantage of Azure's infrastructure platform.