visual studio 2017 sql server system.data

Easy deployment: Once you have the Python model ready, deploying it in production is now as easy as embedding it in a T-SQL script, and then any SQL client application can take advantage of Python-based models and intelligence by a simple stored procedure call.

This new capability brings Python to the data and runs code inside secure SQL Server using the proven extensibility mechanism built in SQL Server 2016. This eliminates barriers of security, compliance, governance, integrity, and a host of similar issues related to moving vast amounts of data around. Instead, you can build Python applications in the database.

Elimination of data movement: You no longer need to move data from the database to your Python application or model.The Python integration in SQL Server provides several advantages: We are renaming R Services to Machine Learning Services, and R and Python are two options under this feature.

The addition of Python builds on the foundation laid for R Services in SQL Server 2016 and extends that mechanism to include Python support for in-database analytics and machine learning. Starting with the CTP 2.0 release of SQL Server 2017, you can now bring Python-based intelligence to your data in SQL Server. Python is one of the most popular languages for data science and has a rich ecosystem of powerful libraries. We are excited to share the preview release of in-database analytics and machine learning with Python in SQL Server.

Visual studio 2017 sql server system.data