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SQL Server 2017 Machine Learning Services with R

You're reading from   SQL Server 2017 Machine Learning Services with R Data exploration, modeling, and advanced analytics

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Product type Paperback
Published in Feb 2018
Publisher Packt
ISBN-13 9781787283572
Length 338 pages
Edition 1st Edition
Languages
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Authors (2):
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Julie Koesmarno Julie Koesmarno
Author Profile Icon Julie Koesmarno
Julie Koesmarno
Toma≈æ Ka≈°trun Kaštrun Toma≈æ Ka≈°trun Kaštrun
Author Profile Icon Toma≈æ Ka≈°trun Kaštrun
Toma≈æ Ka≈°trun Kaštrun
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Table of Contents (12) Chapters Close

Preface 1. Introduction to R and SQL Server 2. Overview of Microsoft Machine Learning Server and SQL Server FREE CHAPTER 3. Managing Machine Learning Services for SQL Server 2017 and R 4. Data Exploration and Data Visualization 5. RevoScaleR Package 6. Predictive Modeling 7. Operationalizing R Code 8. Deploying, Managing, and Monitoring Database Solutions containing R Code 9. Machine Learning Services with R for DBAs 10. R and SQL Server 2016/2017 Features Extended 11. Other Books You May Enjoy

Integrating the R model for fast batch prediction

Before continuing to the next steps, please follow the Prerequisite - Prepare the data section.

Step 1 – Train and save a real-time scoring model using T-SQL

In this step, you can create a predictive model for real-time scoring and native scoring, and optionally for the AUC, into a table via a stored procedure. The goal is to build a model that is reusable. You can skip this step if there is an existing compatible model that was created and stored in a table in SQL Server already.

The following stored procedure uses rxSerializeModel, which lets you serialize an R model in raw format. This then allows you to save the model in the VARBINARY format, which can be loaded...

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