Amazon AWS is the other main player in cloud computing. They have many of the same offerings as Azure, but also some that are different. We’ll take a look at the basics such as SQS, S3, EC2, SNS, and SES, to see how you can quickly and easily incorporate the cloud into your existing applications.

This session introduces the various requirements for a system or application to support an elastically scalable environment. The session goes on to detail architecture and design features to support scalability. Finally, we'll dive into some of the details of implementing the features--with examples in .NET--that make a system or application scalable, some areas of difficulty, and how to be more successful.

Ever wondered how Azure provides a scalable environment for thousands of applications? This session takes an intermediate look at Azure Service Fabric. You’ll see how Azure uses established patterns and principles to provide an environment that is massively scalable. You’ll also see how that environment is made available to the public as the Service Fabric platform. The session includes some examples of implementing services in the fabric, the various types of services, and recommendations on how to approach creating and architecting services to support scalability and make the most of Service Fabric.

The rise of machine learning has produced an explosion of APIs to make your applications more intelligent. In this session, you will learn about the 20+ different Cognitive Services APIs that provide object recognition, face detection and identification, emotion recognition, OCR, computer vision, video services, speech and speaker recognition, language understanding, text analytics, sentiment analysis, knowledge exploration, search services, and more. You can also leverage these services in conjunction with the Microsoft Bot Framework to build an intelligent assistant. You will see powerful demos of these capabilities, experience the simplicity of calling this code, and walk away with ideas on how to leverage this functionality in your own applications.

Using an EPOC headset from Emotiv, I have captured 14 channels of EEG (brain waves) while subjects lied and answered truthfully to a series of questions. I fed this labelled dataset into Azure Machine Learning to build a classifier which predicts whether a subject is telling the truth or lying. In this session, I will share my results on this “lie detector” experiment. I will show my machine learning model, data cleaning process, and results, along with discussing the limitations of my approach and next steps/resources. Attendees will gain exposure to the Emotiv EPOC headset and Azure Machine Learning.

Interested in doing machine learning in the cloud? In this demo-heavy talk, I will set the stage with some information on the different types of machine learning (clustering, classification, regression, and anomaly detection) supported by Azure Machine Learning and when to use each. Then, for the majority of the session, I’ll demonstrate using Azure Machine Learning to build a model which predicts survival of individuals on the Titanic (one of the challenges on the Kaggle website). I'll talk through how I analyze the given data and why I choose to drop or modify certain data, so you will see the entire process from data import to data cleaning to building, training, testing, and deploying a model. You’ll leave with practical knowledge on how to get started and build your own predictive models using Azure Machine Learning.