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Introducing Microsoft SQL Server 2019

You're reading from   Introducing Microsoft SQL Server 2019 Reliability, scalability, and security both on premises and in the cloud

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781838826215
Length 488 pages
Edition 1st Edition
Languages
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Authors (8):
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Allan Hirt Allan Hirt
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Allan Hirt
Dustin Ryan Dustin Ryan
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Dustin Ryan
Mitchell Pearson Mitchell Pearson
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Mitchell Pearson
Kellyn Gorman Kellyn Gorman
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Kellyn Gorman
Dave Noderer Dave Noderer
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Dave Noderer
Buck Woody Buck Woody
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Buck Woody
Arun Sirpal Arun Sirpal
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Arun Sirpal
James Rowland-Jones James Rowland-Jones
Author Profile Icon James Rowland-Jones
James Rowland-Jones
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Table of Contents (15) Chapters Close

Preface 1. Optimizing for performance, scalability and real‑time insights 2. Enterprise Security FREE CHAPTER 3. High Availability and Disaster Recovery 4. Hybrid Features – SQL Server and Microsoft Azure 5. SQL Server 2019 on Linux 6. SQL Server 2019 in Containers and Kubernetes 7. Data Virtualization 8. Machine Learning Services Extensibility Framework 9. SQL Server 2019 Big Data Clusters 10. Enhancing the Developer Experience 11. Data Warehousing 12. Analysis Services 13. Power BI Report Server 14. Modernization to the Azure Cloud

Machine learning using the Machine Learning Services extensibility framework

The machine learning services extensibility framework is an architecture that allows a language processing environment (such as the R, Python, or Java runtimes) to run alongside the SQL Server engine. Using a service, the language runtime can then accept, process, and pass back data to and from SQL Server securely and quickly. We'll examine the complete Machine Learning Services extensibility framework architecture once you have learned more about working with the languages.

Python, R, and Java all use the Machine Learning Services extensibility framework to run machine learning code. The following sections will provide an overview of how that process works from the development process, starting with R.

Note

You can use two general methods to code machine learning systems in SQL Server: Writing the code in-database; or creating Python and R code locally and processing the calls on the database...

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