This article describes the machine learning services provided in SQL Server 2017, which support in-database use of the Python and R languages. The integration of SQL Server with open source languages popular for machine learning makes it easier to use the appropriate tool--SQL, Python, or R--for data exploration and modeling. R and Python scripts can also be used in T-SQL scripts or Integration Services packages, expanding the capabilities of ETL and database scripting. What has this to do with stone soup, you ask? It's a metaphor, of course, but one that captures the essence of why SQL Server works so well with Python and R. To illustrate the point, I'll provide a simple walkthrough of data exploration and modeling combining SQL and Python, using a food and nutrition analysis dataset from the US Department of Agriculture. You might have heard that data science is more of a craft than a science. Many ingredients have to come together efficiently, to process intake data and generate models and predictions that can be consumed by business users and end customers. However, what works well at the level of "craftsmanship" often has to change at commercial scale. Much like the home cook who has ventured out of the kitchen into a restaurant or food factory, big changes are required in the roles, ingredients, and processes. Moreover, cooking can no longer be a "one-man show;" you need the help of professionals with different specializations and their own tools to create a successful product or make the process more efficient. These specialists include data scientists, data developers and taxonomists, SQL developers, DBAS, application developers, and the domain specialists or end users who consume the results. Any kitchen would soon be chaos if the tools used by each professional were incompatible with each other, or if processes had to be duplicated and slightly changed at each step. What restaurant would survive if carrots chopped up at one station were unusable at the next?

Link: Machine Learning Basics - SQL Server 2017, R, Python & T-SQL This article explains the basics of SQL Server Machine Learning Services. Also you get to compare the functional equivalent of both languages with reference manuals available in this course. These examples range from basics to advanced complex visualizations. Machine Learning Basics with SQL Server 2017, R and Python is a course in which a student having no experience / awareness of Machine Learning / R / Python / SQL Server 2017 Machine Learning Services would be trained step by step to a level where the student is confident to independently work independently with each of them. Course includes practical hands-on queries with explanation and analysis, and theoretical coverage of key concepts.

SQL Server 2017 Machine Learning Services is an add-on to a database engine instance, used for executing R and Python code on SQL Server. The code runs in an extensibility framework, isolated from core engine processes, but fully available to relational data as stored procedures, as T-SQL script containing R or Python statements, or as R or Python code containing T-SQL. If you previously used SQL Server 2016 R Services, Machine Learning Services in SQL Server 2017 is the next generation of R support, with updated versions of base R, RevoScaleR, MicrosoftML, and other libraries introduced in 2016. The key value proposition of Machine Learning Services is the power of its enterprise R and Python packages to deliver advanced analytics at scale, and the ability to bring calculations and processing to where the data resides, eliminating the need to pull data across the network. SQL Server 2017 supports R and Python.

Native scoring is a much overlooked feature in SQL Server 2017 (available only under Windows and only on-prem), that provides scoring and predicting in pre-build and stored machine learning models in near real-time. Depending on the definition of real-time, and what does it mean for your line of business, I will not go into the definition of real-time, but for sure, we can say scoring 10.000 rows in a second from a mediocre client computer (similar to mine) . Native scoring in SQL Server 2017 comes with couple of limitations, but also with a lot of benefits. Overall, if you are looking for a faster predictions in your enterprise and would love to have a faster code and solution deployment, especially integration with other applications or building API in your ecosystem, native scoring with PREDICT function will surely be advantage to you. Although not all of the predictions/scores are supported, majority of predictions can be done using regression models or decision trees models (it is estimated that both type (with derivatives of regression models and ensemble methods) of algorithms are used in 85% of the predictive analytics).

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.

Join us for our discussion on Machine Learning Services for SQL Server 2017 which provides a platform for developing and deploying intelligent applications that uncover new insights. You can use the rich and powerful R and Python languages and the many packages from the community to create models and generate predictions using your SQL Server data. Since machine learning is integrated with SQL Server, you can keep analytics close to the data and eliminate the costs and security risks associated with data movement. SQL Server supports open source R and Python libraries with a comprehensive set of tools and technologies that offer superior performance, scalability, security, reliability, and manageability. Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business.

As you explore machine learning scenarios in your cloud applications, the speed of your scoring operations is critical. Native scoring, a feature available in SQL Server 2017, supports any operation you might run in R, ranging from simple functions to training complex machine learning models, enabling faster prediction performance in your enterprise production scenarios. In this live webinar with interactive Q&A, you will learn about native scoring and Machine Learning Services on SQL Server 2017, how these features can benefit your organization, and how you can use them to implement you own machine learning scenarios. Native scoring is a feature that is available today on SQL Server on Linux, and Machine Learning Services is a feature that will soon become available on Linux.

While SQL Server 2017 continues to get attention for opening up to Linux, many of Microsoft's database advances revolve around various ways the company is opening up analytics on its flagship database. Trends come and go, but your DB strategy shouldn't be a flavor of the month. Learn why you shouldn't get distracted by new DB technology, how Facebook is using a RDBMS to do the data slicing and dicing they can't in Hadoop, and more. You forgot to provide an Email Address. This email address doesn't appear to be valid.

At a fireside chat at Microsoft's Ignite conference in Orlando this week, CEO Satya Nadella conceded to moderator Walter Isaacson that one of his goals was for Microsoft to once again become a curious company that admits it doesn't know it all. Part of being curious is coming out of its comfort zone, something we saw additional evidence of with announcements, especially with SQL Server, coming out of the conference this week. Release of SQL Server 2017 on both Windows and Linux was obviously not a surprise -- Microsoft declared its intentions roughly 18 months ago. But it was the latest step in a process of making Linux a first-class citizen, especially on the platform that really matters to Microsoft, the Azure cloud (for the record, SQL Server on Linux is also available in an on-premise edition). The definitive SQL Server 2017 story has already been told by Big on Data bro Andrew Brust on these pages.

Data is an important asset for every business. Getting from raw data to insights empowers business decision makers to gain a deeper understanding into each aspect of the business and helps them react to new business situations quickly. For example, consider a retail scenario. The business analyst notices that sales are dropping for specific retail stores. The business analyst wants to drill down to understand the details on what's causing the drop in sales.