Steven Astorino, VP IBM Db2 LUW and Cognos, presents "Infuse AI into your Applications" with Db2, Business Analytics, and more. Create dashboards automatically with AI. Libby Ingrassia presents the 2020 Call for IBM Champions. Please join Al Martin, Data and AI Man Extraordinaire, as he discusses how profoundly Information Architecture provides the foundation to climb the ladder to AI, and what steps are available at every stage of your company's AI journey… CLICK TO REGISTER Top blogger Raghu Cherukuru, aka "Raghu on Tech", shares his best tips and tricks for analyzing and improving Db2 LUW database performance. CLICK TO REGISTER and learn more about Raghu!

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned.