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You're reading from  SQL Server 2017 Machine Learning Services with R.

Product typeBook
Published inFeb 2018
Reading LevelIntermediate
PublisherPackt
ISBN-139781787283572
Edition1st Edition
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Authors (2):
Julie Koesmarno
Julie Koesmarno
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Julie Koesmarno

Julie Koesmarno is a senior program manager in the Database Systems Business Analytics team, at Microsoft. Currently, she leads big data analytics initiatives, driving business growth and customer success for SQL Server and Azure Data businesses. She has over 10 years of experience in data management, data warehousing, and analytics for multimillion-dollar businesses as a SQL Server developer, a system analyst, and a consultant prior to joining Microsoft. She is passionate about empowering data professionals to drive impacts for customer success and business through insights.
Read more about Julie Koesmarno

Tomaž Kaštrun
Tomaž Kaštrun
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Tomaž Kaštrun

Toma Katrun is a SQL Server developer and data scientist with more than 15 years of experience in the fields of business warehousing, development, ETL, database administration, and query tuning. He holds over 15 years of experience in data analysis, data mining, statistical research, and machine learning. He is a Microsoft SQL Server MVP for data platform and has been working with Microsoft SQL Server since version 2000. He is a blogger, author of many articles, a frequent speaker at the community and Microsoft events. He is an avid coffee drinker who is passionate about fixed gear bikes.
Read more about Tomaž Kaštrun

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Summary

In this chapter, you learned the steps required to integrate an existing predictive analytics R code into resides outside of SQL Server R with the Extensibility Framework. You have also seen the simplicity and the power of the new PREDICT function in SQL Server 2017, which allows native scoring without having to install R. Managing the security required to run predictive analytics workloads is also important in prediction operations. You have learned how to add SQL queries to R projects using RTVS. Finally, you've discovered the different possibilities for integrating R code and prediction operations into your existing workflows as SQL Server stored procedures, SQL Server Agent jobs, PowerShell scripts, and SSIS projects.

With these new skills, we are ready for the next building block in managing data science solutions as part of database lifecycle: management practices...

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SQL Server 2017 Machine Learning Services with R.
Published in: Feb 2018Publisher: PacktISBN-13: 9781787283572

Authors (2)

author image
Julie Koesmarno

Julie Koesmarno is a senior program manager in the Database Systems Business Analytics team, at Microsoft. Currently, she leads big data analytics initiatives, driving business growth and customer success for SQL Server and Azure Data businesses. She has over 10 years of experience in data management, data warehousing, and analytics for multimillion-dollar businesses as a SQL Server developer, a system analyst, and a consultant prior to joining Microsoft. She is passionate about empowering data professionals to drive impacts for customer success and business through insights.
Read more about Julie Koesmarno

author image
Tomaž Kaštrun

Toma Katrun is a SQL Server developer and data scientist with more than 15 years of experience in the fields of business warehousing, development, ETL, database administration, and query tuning. He holds over 15 years of experience in data analysis, data mining, statistical research, and machine learning. He is a Microsoft SQL Server MVP for data platform and has been working with Microsoft SQL Server since version 2000. He is a blogger, author of many articles, a frequent speaker at the community and Microsoft events. He is an avid coffee drinker who is passionate about fixed gear bikes.
Read more about Tomaž Kaštrun