Another big application of this pattern is routing support tickets. Although this sounds simple, it's actually rather complicated. For those of you who run consumer apps or services, you must be accustomed to getting tons of customer feedback. If you process this feedback right and in a timely manner, you can deliver an amazing customer experience. But one big issue is that, if your app or service were to experience exponential growth, your support costs also go up exponentially, because as a business you do want to try your best to look at every support issue/ticket.

The first Microsoft Machine Learning & Data Science Summit concluded earlier this week in Atlanta. Speakers at the Summit included thought leaders, engineers and practitioners from Microsoft and our customers and partners. The Summit featured the very latest in the areas of big data, machine learning and artificial intelligence, and covered a swathe of techniques including several popular Open Source technologies. The Summit gave participants a chance to connect in person with peer engineers, data scientists, ML practitioners, managers and decision makers, and to share tips and tricks. Participants were able to connect with speakers at a specially organized "Meet the Speakers" forum, where they even got answers to questions concerning their specific challenges or work projects.

This is the pattern where intelligence lives with the data in the database. Imagine a core transactional enterprise application built with a database such as SQL Server. What if you could embed intelligence, i.e. advanced analytics algorithms plus data transformations, within the database itself, to make every transaction intelligent in real time? That's now possible for the first time with R and ML built into SQL Server 2016. At the Summit, we'll illustrate this with a fascinating demo of real-time predictive fraud detection and scoring in SQL Server.

This is the pattern where intelligence lives with the data in the database. Imagine a core transactional enterprise application built with a database such as SQL Server. What if you could embed intelligence, i.e. advanced analytics algorithms plus data transformations, within the database itself, to make every transaction intelligent in real time? That's now possible for the first time with R and ML built into SQL Server 2016. At the Summit, we'll illustrate this with a fascinating demo of real-time predictive fraud detection and scoring in SQL Server.