Using Python In SQL Server 2017

Emma Stewart has a post covering setup and configuration of SQL Server 2017 Machine Learning Services and using Python within SQL Server:

One of the new features of SQL Server 2017 was the ability to execute Python Scripts within SQL Server. For anyone who hasn’t heard of Python, it is the language of choice for data analysis. It has a lot of libraries for data analysis and predictive modelling, offers power and flexibility for various machine learning tasks and is also a much simpler language to learn than others.

The release of SQL Server 2016, saw the integration of the database engine with R Services, a data science language. By extending this support to Python, Microsoft have renamed R Services to ‘Machine Learning Services’ to include both R and Python.

The benefits of being able to run Python from SQL Server are that you can keep analytics close to the data (if your data is held within a SQL Server database) and reduce any unnecessary data movement. In a production environment you can simply execute your Python solution via a T-SQL Stored Procedure and you can also deploy the solution using the familiar development tool, Visual Studio.

ML Services is a great addition to SQL Server.

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