Machine Learning Services (R, Python) in SQL Server 2017

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                Machine Learning Service (R,Python )in SQL Server 2017


 SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. The feature includes microsoft R python packages for high-performance predictive analytics and machine learning. Code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL.
If you previously used , Machine Learning SQL server 2016 R service  in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016.
In Azure SQL Database machine learning service with R is currently in public preview. 

Bring  compute power to the data

The key value proposition of Machine Learning Services is the power of its enterprise R and Python packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network. This provides multiple advantages:
  • Data security. Bringing R and Python execution closer to the source of data avoids wasteful or insecure data movement.
  • Speed. Databases are optimized for set-based operations. Recent innovations in databases such as in-memory tables make summaries and aggregations lightning, and are a perfect complement to data science.
  • Ease of deployment and integration. SQL Server is the central point of operations for many other data management tasks and applications. By using data that resides in the database or reporting warehouse, you ensure that the data used by machine learning solutions is consistent and up-to-date.
  • Efficiency across cloud and on-premises. Rather than process data in R or Python sessions, you can rely on enterprise data pipelines including Integration Services and Azure Data Factory. Reporting of results or analyses is easy via Power BI or Reporting Services.
By using the right combination of SQL and R for different data processing and analytical tasks, both data scientists and developers can be more productive.

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