3 Key Practical Benefits in Database Machine Learning Services Introduced in SQL Server 2017

The article sheds light on the benefits from Machine learning services which were introduced in SQL Server 2017.

SQL Server 2017 is now generally available with users, they can use Machine Learning Services for production purposes. Machine Learning services are basically newly branded ‘R-Services’. So SQL Server now uses three data science languages which enable users to use the AI & ML packages from open source database. Thus SQL server emerges out as the best commercial Database with built in AI.Key Practical Benefits In Database Machine Learning Services Introduced In SQL Server 2017

The other benefitting features could include elimination of data movement, enhanced security, better performance, ease of deployment and better scale. These factors make SQL server an essential platform and users utilize its services in many fields like financial banking models, pricing optimizations models, analytical solutions and real estate insights, enhanced and accurate target marketing, etc.

1. Python Integration

Machine Learning Services in SQL server supports in-database Python and thus it attracts the large chunk of python developers & ML practitioners as they can now use both SQL Server as well as Python code. Since the latest innovations from Microsoft like revoscalepy and microsoftml libraries created an open source ecosystem for SQL Server developers to access AI libraries and extensive Python ML. It’s now easier to develop intelligent and efficient apps with insightful in-database analytics.

In general, SQL Python integration is not merely limited to providing AI solutions and Machine learning but also useful for extensive data analysis using the combination of Python and SQL in effective ways.

2. R Package Management

R Package management has been considerably improved for SQL Server. R package has a vibrant community with more than a thousand open-source packages. Using the rich set of R functions for package management, users can install/uninstall and control packages through various roles. To install R packages on SQL servers, one can use TSQL Commands from Create External Library.

3. Machine Learning Services

Machine Learning ServicesSince the SQL server 2017 was made available on multiple platforms, Microsoft also made the Machine learning Server generally available. It’s underlying software integrated with SQL Server and is called Machine learning services. Basically, it is the newly transformed Microsoft R server with a better and more flexible platform which offers its users the choice of Python language and R . Together, they can be used for creating algorithmic innovations from Microsoft as well as Open Source platforms.

Customers can thus create interactive and portable models without depending on Data’s location and can run operations on models on SQL server and other platforms. It makes the process easy and effective for business applications. There are majorly two algorithmic innovations of Machine Servers which are as follows-

  1. Revoscalepy – It has the MS’s Parallel External Memory Algorithms and a set of APIs for ETL.
  2. microsoftml – It is a set of ML algorithms and also contains trained models which can extract features from pictures.

SQL’s Machine learning Services are available in all of the SQL Server 2017 editions on windows and users can download these from the developer or free express editions.  Despite its array of advances, the 2017 iteration still remains vulnerable to incidents of data corruption and companies need to prepare themselves do deal with corrupt SQL Server files.

Author Introduction:

Victor Simon is a data recovery expert in DataNumen, Inc., which is the world leader in data recovery technologies, including fix Access and sql recovery software products. For more information visit https://www.datanumen.com/

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