John Garland is a Principal Consultant and Instructor at Wintellect and has been developing software professionally since the 1990’s. His consulting clients range from small businesses to Fortune-500 companies and his work has been featured at Microsoft conference keynotes and sessions. John lives in Cumming, GA with his wife and daughter and is a graduate of the University of Florida with a Bachelor’s degree in Computer Engineering. He is the author of the book “Windows Store Apps Succinctly” and co-author of the book "Programming the Windows Runtime by Example". John is currently a Windows Platform Development MVP and a member of the Microsoft Azure Insiders.
Adding personalized experiences is often a critical part of creating an application, and the key to personalization is being able to identify your users. However, properly managing user identities can be difficult, and getting it wrong can cost you users due to usability problems, or worse, can expose your users to harm if their identity information is not properly protected. Azure Active Directory B2C provides you the ability to integrate a ready-made identity platform into your application, with options for integration with social identity providers, application-local accounts, customized workflows, and a user interface that can integrate into your app's layout and design. In this talk you will learn how you can integrate Azure Active Directory B2C into a variety of applications, and several of the ways you can customize the experience to best support both your users' and your application's needs.
One of the consequences of the increased ability to store massive amounts of data provided by cloud solutions like Azure is growing interest and tooling around the processing of this data. Azure Machine Learning is one such tool, offering easy access to the once-arcane science of Machine Learning. This talk will provide an overview of Azure Machine Learning tools, featuring a walk-through that will show how you can use the Azure ML Studio tooling to create and train an ML model. Once it has been trained on existing data, you will see how it is possible to operationalize this model, publishing it as a REST endpoint that can be called by other applications in order to make predictions as new data becomes available.