{"id":20797,"date":"2017-09-26T09:30:51","date_gmt":"2017-09-26T16:30:51","guid":{"rendered":"https:\/\/blogs.technet.microsoft.com\/dataplatforminsider\/?p=20797"},"modified":"2024-01-22T22:51:02","modified_gmt":"2024-01-23T06:51:02","slug":"in-database-machine-learning-in-sql-server-2017","status":"publish","type":"post","link":"https:\/\/www.microsoft.com\/en-us\/sql-server\/blog\/2017\/09\/26\/in-database-machine-learning-in-sql-server-2017\/","title":{"rendered":"In-database Machine Learning in SQL Server 2017"},"content":{"rendered":"

This post is authored by Sumit Kumar, Senior Program Manager, Microsoft and Nellie Gustafsson, Program Manager, Microsoft<\/em><\/p>\n

We are excited to announce the general availability of SQL Server 2017 and Machine Learning Services. You can start using Python-based in-database Machine Learning Services for production usage now. With support for both R and Python, we have rebranded \u2018R Services\u2019 to \u2018Machine Learning Services\u2019. SQL Server now supports the three most popular<\/a> data science languages and enables you to use the latest AI and ML packages from the open source world in-database, across ALL editions on Windows \u2013 making SQL Server 2017 the commercial database with built-in AI.<\/p>\n

As we have covered in previous posts<\/a>, there are many advantages of using this technology, such as the elimination of data movement, ease of deployment, improved security and better scale and performance. These abilities make SQL Server a powerful enterprise platform for machine learning. Examples of what some customers have built using Machine Learning Services:<\/p>\n