SQL Server 2017 Machine Learning Services with R: Data exploration, modeling, and advanced analytics

SQL Server 2017 Machine Learning Services with R: Data exploration, modeling, and advanced analytics

AUTHOR: Tomaz Kastrun; Julie Koesmarno

PUBLISHER: Packt

PAGES: 338

ISBN-10: 1787283577; ISBN-13: 978-1787283572

Develop and run efficient R scripts and predictive models for SQL Server 2017

Key Features
* Combine the power of R and SQL Server 2017 to build efficient and cost-effective data science solutions
* Leverage capabilities of R Services to perform advanced analytics from data exploration to predictive modeling
* Get up and running with SQL Server 2017 Machine Learning Services with R, as part of database solutions with continuous integration and delivery

Book Description
R Services, one of the most anticipated features in SQL Server 2016, improved significantly and rebranded as SQL Server 2017 Machine Learning Services. Prior to SQL Server 2016, many developers and data scientists were already using R to connect to SQL Server in siloed environments that left a lot to be desired, in order to perform additional data analysis, superseding SSAS Data Mining or additional CLR programming functions. With R integrated within SQL Server 2017, these developers and data scientists can now benefit from its integrated, effective, efficient, and more streamlined analytics environment.

SQL Server 2017 Machine Learning Services with R gives you foundational knowledge and insight to help you understand SQL Server 2017 Machine Learning Services with R. This book provides practical examples on how to implement, use, and understand SQL Server and R integration in corporate environments, and also provides explanations and underlying motivations. It covers installing machine learning services, along with maintaining, deploying, and managing code, and monitoring your services.

Once you ve got to grips with basics, you ll deep dive into predictive modeling and the RevoScaleR package, and gain insight into operationalizing code and exploring and visualizing data. In the concluding chapters, SQL Server 2017 Machine Learning Services with R covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.

What you will learn
* Get an overview of SQL Server 2017 Machine Learning Services with R
* Manage SQL Server Machine Learning Services from installation to configuration and maintenance
* Handle and operationalize R code
* Explore RevoScaleR R algorithms and create predictive models
* Deploy, manage, and monitor database solutions with R
* Extend R with SQL Server 2017 features
* Explore the power of R for database administrators

Who This Book Is For
SQL Server 2017 Machine Learning Services with R is for data analysts, data scientists, and database administrators with some or no experience in R but who are eager to easily deliver practical data science solutions in their day-to-day work (or future projects) using SQL Server.

About the Author
Tomaz Kastrun 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.

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.

Disclaimer

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