SQL Server R Services is now SQL Server Machine Learning Services and supports Python. First, Nagesh Pabbisetty and Sumit Kumar talk about Python support:
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. We are renaming R Services to Machine Learning Services, and R and Python are two options under this feature.
The Python integration in SQL Server provides several advantages:
Elimination of data movement: You no longer need to move data from the database to your Python application or model. Instead, you can build Python applications in the database. This eliminates barriers of security, compliance, governance, integrity, and a host of similar issues related to moving vast amounts of data around. 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.
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.
Enterprise-grade performance and scale: You can use SQL Server’s advanced capabilities like 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. Using these with the latest innovations in the open source Python world allows you to bring unparalleled selection, performance, and scale to your SQL Python applications.
Rich extensibility: You can install and run any of the latest open source Python packages in SQL Server to build deep learning and AI applications on huge amounts of data in SQL Server. Installing a Python package in SQL Server is as simple as installing a Python package on your local machine.
Wide availability at no additional costs: Python integration is available in all editions of SQL Server 2017, including the Express edition.
Nagesh Pabbisetty also announces Microsoft R Server 9.1:
We took the first step with Microsoft R Server 9.0, and this follow on release includes significant innovations such as:
New machine learning enhancements and inclusion of pre-trained cognitive models such as sentiment analysis & image featurizers
SQL Server Machine Learning Services with integrated Python in Preview
Enterprise grade operationalization with real-time scoring and dynamic scaling of VMs
Deep customer & ISV partnerships to deliver the right solutions to customers
A panoply of sources to help you get started with ease
And Joseph Sirosh indicates that AI is where the money is:
So today it’s my pleasure to announce the first RDBMS with built-in AI—a production-quality Community Technology Preview (CTP 2.0) of SQL Server 2017. In this preview release, we are introducing in-database support for a rich library of machine learning functions, and now for the first time Python support (in addition to R). SQL Server can also leverage NVIDIA GPU-accelerated computing through the Python/R interface to power even the most intensive deep-learning jobs on images, text, and other unstructured data. Developers can implement NVIDIA GPU-accelerated analytics and very sophisticated AI directly in the database server as stored procedures and gain orders of magnitude higher throughput. In addition, developers can use all the rich features of the database management system for concurrency, high-availability, encryption, security, and compliance to build and deploy robust enterprise-grade AI applications.
There’s a lot to digest here.