SQL Server Machine Learning Services
SQL Server Machine Learning Services (R and Python) Documentation
Learn how to use R and Python external libraries and languages on resident, relational data with our quickstarts, tutorials, and how-to articles. R and Python libraries in SQL Server machine learning include base distributions, data science models, machine learning algorithms, and functions for conducting high-performance analytics at scale, without having to transfer data across the network.
For the documentation on Java, see the SQL Server Language Extensions documentation.
|Open-source R, extended with RevoScaleR and Microsoft AI algorithms in MicrosoftML. These libraries give you forecasting and prediction models, statistical analysis, visualization, and data manipulation at scale.
R integration starts in SQL Server 2016 and is also in SQL Server 2017.
|Python developers can use Microsoft revoscalepy and microsoftml libraries for predictive analytics and machine learning at scale. Anaconda and Python 3.5-compatible libraries are the baseline distribution.
Python integration starts in SQL Server 2017.
|RevoScaleR||R||Distributed and parallel processing for R tasks: data transformation, exploration, visualization, statistical and predictive analytics.|
|MicrosoftML||R||Functions based on Microsoft's AI algorithms, adapted for R.|
|olapR||R||Imports data from OLAP cube.s|
|sqlRUtils||R||Helper functions for encapsulating R and T-SQL.|
|revoscalepy||Python||Distributed and parallel processing for Python tasks: data transformation, exploration, visualization, statistical and predictive analytics.|
|microsoftml||Python||Functions based on Microsoft's AI algorithms, adapted for Python.|
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