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.

In Joseph Sirosh's keynote presentation at the Data Science Summit on Monday, Wee Hyong Took demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card transaction being fraudulent, based on attributes like the charge amount and the country of origin. Then, a stored procedure in SQL Server 2016 was used to score transactions streaming into the database at a rate of 3.6 billion per hour. Later in the keynote (starting at 25:00), John Salch, VP of Technology and Platforms at PROS describes using R to determine prices for airline tickets, hotel rooms, and laptops. PROS has been using R for a while in development, but found running R within SQL Server 2016 to be 100 times (not 100%, 100x!) faster for price optimization.

We're happy to announce the availability of a new free ebook, Data Science with Microsoft SQL Server 2016 (ISBN 9781509304318), by Buck Woody, Danielle Dean, Debraj GuhaThakurta, Gagan Bansal, Matt Conners, & Wee-Hyong Tok. The world around us, every business and nearly every industry, is being transformed by technology. This disruption is driven, in part, by the intersection of three trends: a massive explosion of data, intelligence from machine learning and advanced analytics, and the economics and agility of cloud computing. While databases power nearly every aspect of business today, they were not originally designed with this disruption in mind. Traditional databases were about recording and retrieving transactions such as orders and payments very reliably, very securely and efficiently.

In Joseph Sirosh's keynote presentation at the Data Science Summit on Monday, Wee Hyong Took demonstrated using R in SQL Server 2016 to detect fraud in real-time credit card transactions at a rate of 1 million transactions per second. The demo (which starts at the 17:00 minute mark) used a gradient-boosted tree model to predict the probability of a credit card transaction being fraudulent, based on attributes like the charge amount and the country of origin. Then, a stored procedure in SQL Server 2016 was used to score transactions streaming into the database at a rate of 3.6 billion per...

Microsoft last week announced a wave of new features for its data platform, along with the SQL Server 2017 name and what Microsoft calls a "production quality" beta release. Other important changes include a new containerized deployment model for databases, which simplifies installation on Windows and Linux. But it was SQL Server's new machine learning tools that grabbed my attention. Machine learning remains one of Microsoft's big themes for 2017, and it's an important segment of SQL Server 2017. Mixing code and data has always been part of SQL Server, first with T-SQL, then with the Azure-focused U-SQL, which extended T-SQL with C# elements.

Microsoft last week announced a wave of new features for its data platform, along with the SQL Server 2017 name and what Microsoft calls a "production quality" beta release. Other important changes include a new containerized deployment model for databases, which simplifies installation on Windows and Linux. But it was SQL Server's new machine learning tools that grabbed my attention. Machine learning remains one of Microsoft's big themes for 2017, and it's an important segment of SQL Server 2017. Mixing code and data has always been part of SQL Server, first with T-SQL, then with the Azure-focused U-SQL, which extended T-SQL with C# elements.

Microsoft R Server 9.0.1 (MRS9) developer edition, a major update to the enterprise scalable R extension from Microsoft, is now available on the VM. This version brings a lot of exciting changes including several fast ML / deep learning algorithms developed by Microsoft in a new library called Microsoft ML. There's a new architecture and interface for deploying R models and functions as web services, this follows a paradigm and interface library very similar to Azure ML operationalization. The library is called mrsdeploy. We have some R deployment samples for both notebook and R Tools for Visual Studio (RTVS) and RStudio.

With the release of CTP3 SQL Server 2016 and its native In-database support for the open source R language (SQL Server R Services), users can now call both R and RevoScaleR functions and scripts directly from within a SQL query and benefit from multi-threaded and multi-core in-DB computations. The R integration brings the utility of data science to your applications without the need to'export' the data to your R environment. Today, I will use the Adventure Works samples for SQL Server 2016 CTP3 to showcase how we can use SSIS to operationalize a R prediction from doing data preparation, to using the training data to build and save the "trained" model and running prediction using the trained model. In this specific example, we will use the IRIS flower dataset from Ronald Fisher that is built-in dataset from R as our data source and we will load this dataset into a SQL Server table called IRIS_RX_DATA. This will be our training data.

You will learn the main data structure in R, Managing Data with R, Exploring and Understanding Data in R (exploring numeric variable, categorical variable and relationship between variables). Moreover, audience learn how to use some of the main packages in R. You will learn different types of machine learning algorithms and how they work and how they can solve different type of real life problems. At the end of this module you will able to choose right algorithm for right problem.

If you are a data scientist, business analyst or a machine learning engineer, you need model management – a system that manages and orchestrates the entire lifecycle of your learning model. Analytical models must be trained, compared and monitored before deploying into production, requiring many steps to take place in order to operationalize a model's lifecycle. In this blog, I will describe how SQL Server can enable you to automate, simplify and accelerate machine learning model management at scale – from build, train, test and deploy all the way to monitor, retrain and redeploy or retire. SQL Server treats models just like data – storing them as serialized varbinary objects. As a result, it is pretty agnostic to the analytics engines that were used to build models, thus making it a pretty good model management tool for not only R models (because R is now built-in into SQL Server 2016) but for other runtimes as well.