While approaches and capabilities differ, all of these databases allow you to build machine learning models right where your data resides.

But it was SQL Server’s new machine learning tools that grabbed my attention. Machine learning remains one of Microsoft’s big themes for 2017, and it’s an important segment of SQL Server 2017.

Over a month after SQL Server 2019 became generally available, Microsoft has now shined light on some more aspects of the improved machine learning capabilities offered with this release.

Leveraging SQL Server Machine Learning Services (R and Python) for Data Insights SQL Server Machine Learning Services (MLS) has enabled Nithin to run advanced analytics directly within the database.

These fully managed cloud-native smart data services empower developers to build cloud-native database and machine learning (ML) applications in SQL environments with ease.

Microsoft SQL Server Machine Learning and R Services are in-database installations of machine learning that operate within the context of a SQL Server database engine instance, providing R and Python ...

This limitation also applies to SQL Server Machine Learning Services in R and Python. Announcing SQL Server Diagnostic Extensions for SSMS SQL Server creates stack dumps when there are very serious ...

The enhanced feature, now dubbed SQL Server Machine Learning Services (ML Services), allows code in either language to execute natively on the SQL Server itself.

Microsoft Corp. has just released a new version of its R Server analytics platform, the headline act in a number of announcements Wednesday that touch on databases, data analytics and cloud ...

Users will have access to SQL Server Machine Learning Services and Spark Machine Learning, so they'll get insights on all of their data, again, regardless of what form that data comes in.