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SQL Server 2017 Machine Learning Services with R
SQL Server 2017 Machine Learning Services with R

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

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Full star icon Full star icon Full star icon Full star icon Half star icon 4.3 (8 Ratings)
Paperback Feb 2018 338 pages 1st Edition
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Paperback Feb 2018 338 pages 1st Edition
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SQL Server 2017 Machine Learning Services with R

Overview of Microsoft Machine Learning Server and SQL Server

In this chapter, we will look into the reasons why Microsoft decided to add the R language to SQL Server 2016 and what burdens this integration has overcome. We will cover the following topics in this chapter:

  • Addressing analytical barriers and R limitations
  • The Microsoft R Server platform
  • The Microsoft R Services architecture
  • Exploring computational contexts

Analytical barriers

Many companies encounter barriers when trying to analyse their data. These barriers are usually knowledge scarcity (not all departments have the knowledge to analyse the data) and data dispersity (usually data arrives from different sources).

Enterprises divide responsibilities according to the roles or functions of the employees. Such division of work has a positive effect, especially when an enterprise is large. Usually, small to mid-sized enterprises adopt such roles as well, but they are normally granulated on a higher level due to a smaller number of employees.

With rapid market changes, the emergence of new technologies, and the need for faster adaptation, many experts have noticed many of the following barriers:

  • Data scarcity and data dispersity
  • Complex (and many times outdated) architecture
  • Lack of knowledge
  • Low productivity
  • Slow adaptation to market...

The Microsoft Machine learning R Server platform

We have already touched on the concept of R Open and R for enterprise environment briefly. Microsoft Machine Learning R Server is an enterprise server that delivers high dimensional and large datasets that can be processed in parallel, and the workload can be distributed across nodes. R Server can process these parallel and distributed workloads on Windows, Linux servers, or HDFS systems, such as Hadoop, Spark, Teradata, and HD Insight. R Server can achieve parallel and distributed workloads using Microsoft R packages designed to do just that. The RevoScaleR package will give the ability to do highly parallel and distributed computations, statistical analysis, and predictive analytics, as well as machine learning and deep learning.

With the acquisition of the company Revolution Analytics, Microsoft rebranded their two main products...

The Microsoft Machine Learning R Services architecture

The architecture of R Server covers many components needed in order for the communication between R IDE or SQL Server and R engine to work properly.

Several components are involved in order to properly execute Transact SQL, R script, and return all the results back to T-SQL:

Figure 7

Launchpad is a new service in SQL Server 2016 that supports execution of external scripts using the external stored procedure from SQL Server. However, in SQL Server 2017, the Python launcher has also been introduced, making Launchpad generally available to the second non-SQL Server language. The idea behind Launchpad is that since the infrastructure is already prepared, the SQL Server should, in the future, support other languages as well, such as JavaScript and C++, opening this service not only to predictive analytics and machine learning...

Summary

This chapter has given an overview on the ecosystem of the new R Service and the family of R Engine products offered by Microsoft.

When thinking about R and SQL Server, one must keep in mind what the needs in the enterprise will be and how the infrastructure will be prepared in order to leverage the maximum performance from powerful RevoScaleR packages.

In the next chapter, we will see how to use these ScaleR computational functions to deliver better performance results, and how to set up your ecosystem.

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Key benefits

  • Learn how you can combine the power of R and SQL Server 2017 to build efficient, cost-effective data science solutions
  • Leverage the capabilities of R Services to perform advanced analytics—from data exploration to predictive modeling
  • A quick primer with practical examples to help you get up- and- running with SQL Server 2017 Machine Learning Services with R, as part of database solutions with continuous integration / continuous delivery.

Description

R Services was 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 do 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. This book gives you foundational knowledge and insights to help you understand SQL Server 2017 Machine Learning Services with R. First and foremost, the 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;maintaining, deploying, and managing code;and monitoring your services. Delving more deeply into predictive modeling and the RevoScaleR package, this book also provides insights into operationalizing code and exploring and visualizing data. To complete the journey, this book covers the new features in SQL Server 2017 and how they are compatible with R, amplifying their combined power.

Who is this book for?

This book 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.

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

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Feb 27, 2018
Length: 338 pages
Edition : 1st
Language : English
ISBN-13 : 9781787283572
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Oracle
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Product Details

Publication date : Feb 27, 2018
Length: 338 pages
Edition : 1st
Language : English
ISBN-13 : 9781787283572
Vendor :
Oracle
Category :
Languages :
Concepts :
Tools :

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Table of Contents

11 Chapters
Introduction to R and SQL Server Chevron down icon Chevron up icon
Overview of Microsoft Machine Learning Server and SQL Server Chevron down icon Chevron up icon
Managing Machine Learning Services for SQL Server 2017 and R Chevron down icon Chevron up icon
Data Exploration and Data Visualization Chevron down icon Chevron up icon
RevoScaleR Package Chevron down icon Chevron up icon
Predictive Modeling Chevron down icon Chevron up icon
Operationalizing R Code Chevron down icon Chevron up icon
Deploying, Managing, and Monitoring Database Solutions containing R Code Chevron down icon Chevron up icon
Machine Learning Services with R for DBAs Chevron down icon Chevron up icon
R and SQL Server 2016/2017 Features Extended Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.3
(8 Ratings)
5 star 62.5%
4 star 25%
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2 star 0%
1 star 12.5%
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Weiyun Huang Mar 23, 2018
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I am one of the Microsoft developers that contributed to some features described in this book (I got this book from the author, for free, but that doesn't have an effect on my review). Therefore, before reading this book, I didn’t expect to see much I didn't already know. However, I was pleasantly surprised. This book not only tells a complete story – which starts from basic things such as installation, user creation, permission setting and goes all the way to extended features such as Polybase – but also comes with a lot of practical examples that can be utilized in various scenarios. To me that’s the most valuable part, as I am not a data scientist (I did study R for my work but never solved any real problems with it) and I am happy to see that using our product people can do so many things with their data! The book also introduces functionalities from different Microsoft products which help the data scientists on machine learning/data mining tasks (some of them I only heard of and never used myself, and it is very helpful to see detailed instructions and screen shots). Following this book step by step, one can easily learn how to use all those tools and mine their data. I especially like the organization of the chapters – from basic to advanced, each chapter clearly defines prerequisites, provides instructions and examples and gives precise comments. IMHO this book is a good reference for people trying to create their data science solutions.
Amazon Verified review Amazon
Andrej Apr 03, 2018
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This is a great reference for those who work with Microsoft Machine learning server and SQL Server on a regular basis. It is all you need to know in one place. The writing style is very clear and concise, there are a lot of examples that are easily reproducible if you want to try them on your own.
Amazon Verified review Amazon
mjfagan May 26, 2018
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I am currently working on the Data Science professional program by Microsoft and am taking the Analyzing Big Data with Microsoft R for one of the course options (note: this book isn't required for the class--I just like having reference books that supplement what I'm learning). I've bought several R programming books but this is the first and so far, only, book I've been able to find about SQL Server Machine Learning Services with R.I've read through 5 chapters already and I wish I had it months ago when I was getting started programming in R because I had issues here and there with my machine set-up and the book covered some of the problems I had.If you're new to R programming and machine learning--I'd recommend reading other books that go more into detail about the specifics of the R language, data preparation, modeling, etc. This book doesn't go into the R programming language enough for someone new to the subject to get a handle of the kinds of objects in R, what you can and cannot do with each, etc. Nor does it go into the differences of the different types of regression, how to interpret data to determine features to include/not include in your model, etc.The book does a great job of going through everything in the machine learning process in a SQL Server environment from setup to deployment. I'm glad I've got it on my desk.
Amazon Verified review Amazon
NGson Mar 29, 2018
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I am one of the program managers who worked with the release of this feature in SQL Server. (I received a copy this book from the author in exchange for my honest opinion). This book does an excellent job at providing an end-to-end guide to SQL Server Machine Learning Services, covering the feature in the context of the full data science process. It targets the different roles involved in a successful deployment of a Machine Learning project and contains very useful content for each of those roles. The roles I am referring to are Data Scientist, data engineer and database administrator. Another thing that makes this book practically very useful is that the authors provide the code files covered per chapter. This makes this book a great training material for anyone who wants to thoroughly learn how to leverage R and Python in SQL Server.
Amazon Verified review Amazon
Matthias Riekert Oct 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I have more than 5 years of experience with R. However, when I first tried all of Microsoft R's advertised features, it was very difficult to find useful articles, documentation, and blog posts. With the mentioned sources, I also learned for the Exam 773: Analyzing Big Data with Microsoft R. I passed the exam without the book. After buying the book, however, I have the impression that I could have saved a lot of research time.
Amazon Verified review Amazon
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