Microsoft last week announced a wave of new features for its data platform, along with the SQL Server 2017 name and what Microsoft calls a “production quality” beta release. Other important changes include a new containerized deployment model for databases, which simplifies installation on Windows and Linux.But it was SQL Server’s new machine learning tools that grabbed my attention.[ Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Download the InfoWorld megaguide: The best Python frameworks and IDEs. | Download the InfoWorld quick guide: Learn to crunch big data with R. ]
Machine learning remains one of Microsoft’s big themes for 2017, and it’s an important segment of SQL Server 2017. Mixing code and data has always been part of SQL Server, first with T-SQL, then with the Azure-focused U-SQL, which extended T-SQL with C# elements.
SQL Server 2016 added support for embedded R code, and SQL Server 2017 continues that evolution by improving its support for R and adding Python. (By renaming SQL Server 2016’s R Services to Machine Learning Services in SQL Server 2017, Microsoft has made clear where it’s aiming its SQL tools.)To read this article in full or to leave a comment, please click here

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