It’s imperative in today's world to be able to make split second decisions based on real-time data. Reports based on batch data are great for looking back at trends and potentially making long-term decision, but old data is in many cases already obsolete, and the opportunity to have an actionable impact on the success of a specific process may have been lost. Wouldn't it also be neat if we could tune into that moving that stream and make predictions on the data as it flows? Let's have a look at how we can do that. In this session we'll be training a custom machine learning model using ML.NET, deploying a real-time data pipeline with Azure Stream Analytics to the cloud and finally setting up real-time inference by leveraging the newly released C# UDFs. Beware, there will be live coding!
Alexander is a Microsoft MVP, and a Managing Consultant and .NET Xpert at Excella, based out of Washington DC. He is an international speaker, and have spoken at conferences such as NDC London, ProgNET, Big Data Europe, .NET Jetbrains Global Online Day, Beer City Code, Music City Tech, Code PaLOUsa, Code On The Beach and Philly.NET. He's the organizer of the .NET DC User Group and a member of the .NET Foundation. He is passionate about .NET and cloud architecture, and constantly attempts to push his knowledge on data science and machine learning. When not coding, you'll find him out running or enjoying a good IPA! Say hi on Twitter @alexslotte!
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