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

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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

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.

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

What do you get with eBook?

Instant access to your Digital eBook purchase

Download this book in EPUB and PDF formats

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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SQL Server 2017 Machine Learning Services with R

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Hands-On Data Science with SQL Server 2017

€36.99

Total € 115.97

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Weiyun Huang Mar 23, 2018

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

Andrej Apr 03, 2018

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.

mjfagan May 26, 2018

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.

NGson Mar 29, 2018

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.

Matthias Riekert Oct 08, 2019

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.

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

What do you get with eBook?

SQL Server 2017 Machine Learning Services with R

Overview of Microsoft Machine Learning Server and SQL Server

Analytical barriers

The Microsoft Machine learning R Server platform

The Microsoft Machine Learning R Services architecture

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