Microsoft has announced it's latest SQL Server 2017 CTP 2.1 version on 17 May 2017 which is available both in Windows and Linux. This blog will focus on Machine learning capabilities using Python in SQL Server 2017 CTP 2.1 .

In SQL Server 2017 R and Python languages can be used for statistical analysis. Both SQL Server professionals and data scientists can work on in-database analytics and machine learning with Python. Installation time R and Python are two options under this feature have option to select any one or both.

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Easy installation features with any additional open source Python package, including the modern deep learning packages like Cognitive Toolkit and TensorFlow to run in SQL Server. Taking advantage of these packages, you can build and deploy GPU-powered deep learning database applications. The standard open source CPython interpreter (version 3.5) and some Python packages commonly used for data science are downloaded and installed during SQL Server setup if you choose the Python option in the feature tree. Python-based applications and set up policies and runtime behaves can be administrable using SQL Server. You can manage, secure, and govern the Python runtime to control how the critical system resources on the database machine are used. Security is ensured by mechanisms like process isolation, limited system privileges for Python jobs, and firewall rules for network access. Application developers can use models by calling a stored procedure that has Python script embedded in it. without deep knowledge of the Python models. Also it is possible to use rage both R and Python models in the same application by stored procedure calls.

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Elimination of data movement: Option is in your hand to develop python application in database that reduce data movement from database to python application. Beside that security, compliance, governance, integrity are more easily implementable with enhance capability that brings Python to the data and runs code inside secure SQL Server Easy deployment: Python model can be deployable so easy like embedding it in a T-SQL script .Client application can take advantage of Python-based models and intelligence by a simple stored procedure call. Enterprise-grade performance and scale: performance level advantages say in-memory table and column store indexes with the high-performance scalable APIs in RevoScalePy package. RevoScalePy is modeled after RevoScaleR package in SQL Server R Services. Rich extensibility: Latest Python packages can be installable in SQL Server in a easiest way to build deep learning and AI applications on huge amounts of data in SQL Server. Each edition of SQL Server 2017 support the Python integration without additional cost.