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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
Languages
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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

Starting with SQL Server 2016, R integration became a very important part of the SQL Server platform. Since the public release of SQL server 2016, until February 2018 (the time of writing this), the community had embraced R as well as Python very well, making data exploration and data analysis part of the general database task. Microsoft addressed many of the issues, and broadened the SQL Server as a product. With SQL Server 2017, Python was added as a secondary analytical language, reaching to an even broader community as well as businesses, and at the same time, taking are of data scalability, performance, and security.

In the next chapter, we will cover different R distributions and IDE tools for using R as a standalone or within the SQL Server, and what the differences among them are when deciding which one to choose.

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