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.

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.

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 ...

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.

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 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 ...

Another T-SQL/Data Science integration in SQL Server 2017 is the ability to add R packages for (modules that act as language extensions) via T-SQL's new CREATE EXTERNAL LIBRARY command, and stored ...

Here's one big advantage to moving to Microsoft Azure SQL Database-you never have to patch. That's all well and good, but most of the SQL Server installed license base is still on-premise. If you're ...