Microsoft SQL Server Machine Learning Service

What is SQL Server Machine Learning Services with Python and R?

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Using the AdventureWorks sample database in a practical example

The AdventureWorks (fictitious) company uses a database that stores data about sales and marketing, products, customers and manufacturing. It also contains a view that joins information about the products, such as the product name, category, price, and a brief description.

Currently, AdventureWorks sends the product name and brief description to a marketing firm to develop a campaign to boost sales. The cost for this marketing specialist is significant, and after looking into the capabilities of Generative Pre-trained Transformers (GPT), the company is curious to know if this AI could be used to create starter-text for brochures, automating the first step of the process. GPT-4 is a powerful language model that can generate coherent and creative text from complex input, or prompts.

Instead of hiring a professional writer, they can use SQL Server Machine Learning Services with Microsoft Azure OpenAI Service to automatically generate high-quality starter ad copy based on the product description and other relevant data. This can save time and money on the marketing budget. This approach also has the added benefit of security, since the database stays local, Stored Procedures have high security granularity, and Azure OpenAI Service does not use the information sent for further training.

This process works on SQL Server Machine Learning Services platforms on Windows, Linux, Containers, and Kubernetes clusters, including virtual machines and SQL Server Managed Instance, from version 2019.

During their research, AdventureWorks discovered that it is important to be cautious when generating text using a GPT model. It is possible that the generated text may not always be appropriate or accurate, so it is important to have a system in place for reviewing and approving any changes before they are published.

The proof-of-concept (PoC) for AdventureWorks involves the following requirements:

1. Create a Stored Procedure to accept the model of a given product.
2. Generate marketing brochure text for that product, highlighting its features and benefits, using Azure OpenAI Service’s GPT-4 model.
3. Ensure that the generated text is safe and factual, and that it can be edited before publishing.

Example code

The POC project for AdventureWorks involves the following steps:

1. Install SQL Server Machine Learning Services on a Microsoft Windows, Linux, or Container environment.
2. Create and deploy a model on Azure OpenAI Service, and obtain the model’s name, keys and endpoints.
3. Install the openai Python package using Azure Data Studio or the sqlmlutils package. This enables the code to use OpenAI API in Python scripts.
4. Write a Python script that uses the sp_execute_external_script stored procedure to query the product’s view, send the product name and description as a combined prompt to the Azure OpenAI API. The script should also specify the parameters for the model, such as the engine name (such as `davinci`), the temperature (a measure of randomness), the max tokens (the maximum length of the generated text), and the stop sequence (a token that indicates the end of the text). The script should also handle any errors or warnings from the API and return the generated text as a result set.
5. Execute the Python script from SQL Server Management Studio or Azure Data Studio and review the generated text for each product. Edit the text as needed or re-run the script with different parameters to get different results.
6. Once the result is satisfactory, wrap the code in a stored procedure to take the product’s model number from the user, and ensure the security chain is verified for the Stored Procedure.
7. In the end-user application, display the result for the ad text to the user for editing, and send the edited result to the database for storage.

With that outline, sample code in an Azure Data Studio Jupyter Notebook has been developed for AdventureWorks, allowing descriptive blocks, code, and persistent results display. After testing and editing, the code can be copied to a Transact-SQL script and deployed to testing and then on to production. The end result produces the following starter text to display to the end user for editing and confirmation before being set to the database:

[sic]

Dear Mountain Bike Enthusiast, Are you looking for a bike that can deliver the performance you need without breaking the bank? Look no further than the Mountain-400-W Silver, 38.  This bike is designed to give you the high-level performance you need to tackle any terrain, without the high price tag. With its responsive and maneuverable design, you’ll be able to navigate even the toughest trails with ease.  But it’s not just about performance – the Mountain-400-W Silver, 38 also offers peace-of-mind when you decide to go off-road. With its sturdy construction and reliable components, you can trust this bike to handle anything you throw at it.  So why wait? Order your Mountain-400-W Silver, 38 today and experience the ultimate in off-road performance at a price you can afford. Don’t settle for anything less than the best – choose the Mountain-400-W Silver, 38 and take your mountain biking to the next level.

Other applications of this approach

As you can see, using Microsoft SQL Server Machine Learning Services with Microsoft Azure OpenAI Service has practical applications. In this example we used it to create a marketing brochure text for various products. This is a powerful and creative way to leverage AI for business needs. The resulting text could be sent to an image-generating service (such as DALL-E) to generate images for the brochure. Other data sets in the AdventureWorks database could be used in other applications, such as investigating sales regions and much more.

However, it is important to be cautious and responsible when using AI models, as they may not always produce accurate or appropriate results. You should always review and edit the generated text before publishing it and follow the safety and ethical guidelines of Microsoft Azure OpenAI Service.

Learn more

• What is SQL Server Machine Learning Services with Python and R?
• What’s new in SQL Server Machine Learning Services?
• The Microsoft Azure OpenAI Service.
• Understanding GPTs.

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For those of us who work with SQL Server, there are many cool new capabilities to get familiar with in SQL Server 2019. At the heart of it all is a solution called Big Data Clusters, allowing you to create scalable clusters of SQL Server, Apache Spark, and HDFS containers running on Kubernetes.

That means flexibility in the ways you access the data and relational data side-by-side. Through the cluster, you can query data from external sources. You can also store big data in HDFS managed by SQL Server. At the end of the day, this makes more of your data available, faster and more easily, for machine learning, artificial intelligence, and other advanced analytical tasks.

SQL Server 2019 also provides expanded machine learning capabilities built in. It adds commonly requested features related to the use of R and Python for machine learning. For example, SQL Server 2019 enables SQL Server Machine Learning Services to be installed on Linux. Failover clusters are supported for greater reliability, and new and improved scripting capabilities open new options for generating and enhancing models.

Integration of Python with the SQL server database engine enables you to perform advanced machine learning tasks close to the data rather than moving it around. Insights generated from the Python runtime can be accessed by production applications using standard SQL Server data access methods.

With the addition of partition-based modeling, you can train many small models instead of one large model when using partitioned data. If you have data that breaks out easily using categories such as demographics or regions, partitioning enables you to get more granular with your models without having to break the dataset apart.

As the line between DBA and data scientist continues to blur, most of us will be expected to understand and manage these types of solutions. Microsoft clearly recognizes the importance of machine learning and the need to apply it more easily across different data types—while maintaining the performance and manageability benefits of using SQL Server.

To learn more about what you can do with Microsoft SQL 19, check out the free Packt guide Introducing Microsoft SQL 19. If you’re ready to jump to a fully managed cloud solution, check out the Essential Guide to Data in the Cloud.

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Microsoft Machine Learning Server is your flexible enterprise platform for analyzing data at scale, building intelligent apps, and discovering valuable insights across your business with full support for Python and R. Machine Learning Server meets the needs of all constituents of the process – from data engineers and data scientists to line-of-business programmers and IT professionals. It offers a choice of languages and features and algorithmic innovation that brings the best of open source and proprietary worlds together.

This release is a continuation of the R and Python value that you have come to see as part of Microsoft R and previous releases of Microsoft Machine Learning Server. Machine Learning Server supports the full data science lifecycle of your R and Python-based analytics. Additionally, Machine Learning Server enables operationalization support so you can deploy your models to a scalable grid for both batch and real-time scoring.

What’s new in Machine Learning Server 9.4

We’ve released Microsoft R Open 3.5.2 and 3.5.3 across various supported platforms. Please find more about Microsoft R Open on the Microsoft R Application Network.

New in Machine Learning Server 9.4:

• Based on Microsoft R Open 3.5.2 and Python 3.7.1.
• Support for Spark 2.4. Check out the comprehensive list of supported platforms.
•  Added an option to specify which server type needs to be installed in either R, Python, or both.
• Upgrading to version 9.4 is easier than ever if you already have an older version of Machine Learning Server installed.

Key features of Machine Learning Server

In addition to the new capabilities outlined above, this release has all the familiar value propositions of Machine Learning Server. Microsoft Machine Learning Server includes:

R and Python enabled

Microsoft Machine Learning Server 9.4 includes specialized R packages and Python modules for developing and operationalizing solutions written in R and Python. Machine Learning Server 9.4 comes with version 3.5.2 of the open source R language engine and a run-time infrastructure for R script execution. On the Python side, it comes with an open source distribution of Python and run-time infrastructure for script execution. Machine Learning Server 9.4 also includes a rich set of highly-scalable algorithms such as RevoScaleR, revoscalepy, and microsoftML that can work on data sizes larger than the size of physical memory, and run on a wide variety of platforms in a distributed manner. Learn more about the collection of Microsoft’s custom R packages and Python packages included with the product.

Machine Learning Server bridges these Microsoft innovations and those coming from the open source community (R, Python and AI toolkits) all on top of a single enterprise-grade platform. Any R or Python open source machine learning package can work side by side with any proprietary innovation from Microsoft. Machine Learning Server runs on-premises and in the cloud on a variety of platforms, in a variety of deployments, and can support the need to have machine learning and analytics across multiple platforms, including clustered topologies for Apache Spark™ on Hadoop.

Deploy and consume

Machine Learning Server offers best-in-class operationalization. From the time a machine learning model is completed, it takes just a few clicks to generate web services APIs. These web services are hosted on a server grid on-premises or in the cloud and can then be easily integrated with line-of-business applications. Additionally, Machine Learning Server integrates seamlessly with Azure Active Directory and includes role-based access control to satisfy security and compliance needs of your enterprise. The ability to deploy to an elastic grid lets you scale seamlessly with the needs of your business, both for batch and real-time scoring.

Solution templates

We provide a number of solution templates for popular solutions in different industries such as healthcare, retail, manufacturing, and finance. We have made it easier to find and deploy templates than ever before. Deploying most of our solution templates to Azure is now just one click of a button – just look for the blue Deploy to Azure button.

Getting started

You can immediately download Microsoft Machine Learning Server 9.4 from Visual Studio Dev Essentials, provision it via Machine Learning Server virtual machines in Azure or get it from our Volume License Service Center. Microsoft Machine Learning Server comes packed with the power of the open source R and Python engines, making both R and Python ready for enterprise-class machine learning and advanced analytics. Check out the R Client for Windows, R Client for Linux, and the Machine Learning Server documentation, including installation instructions to learn more.

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We are excited to announce the general availability of SQL Server 2017 and Machine Learning Services. You can start using Python-based in-database Machine Learning Services for production usage now. With support for both R and Python, we have rebranded ‘R Services’ to ‘Machine Learning Services’. SQL Server now supports the three most popular data science languages and enables you to use the latest AI and ML packages from the open source world in-database, across ALL editions on Windows – making SQL Server 2017 the commercial database with built-in AI.

As we have covered in previous posts, there are many advantages of using this technology, such as the elimination of data movement, ease of deployment, improved security and better scale and performance. These abilities make SQL Server a powerful enterprise platform for machine learning. Examples of what some customers have built using Machine Learning Services:

• Pricing optimization models
• Predictive analytics solutions to gain real-estate property insights
• Better and more accurate models for more precisely targeted marketing
• Financial models for banking

In-database Python integration

With the full support of in-database Python in SQL Server Machine Learning Services in SQL Server 2017, the vast population of Python developers and ML practitioners can now leverage the power of SQL Server alongside their Python code. And the SQL Server developers now have access to the extensive Python ML and AI libraries from the open source ecosystem along with the latest innovations from Microsoft (revoscalepy and microsoftml libraries) for developing intelligent applications with in-database analytics.

Python operationalization with T-SQL

Full Python integration with the sp_execute_external_script infrastructure in SQL Server enables the enterprise-grade operationalization of Python models and scripts as simple stored procedures.

Streaming data from SQL to Python processes and MPI ring parallelization support provides much-improved performance to the Python scripts.

Python remote compute in SQL Server

With the SQL Server remote compute context, data scientists and developers can push the compute of Python code to the server from their development environments, to explore data and develop models without the need to move data.

In-database Python integration is not limited to just machine learning and AI solutions – it is equally useful for general purpose data analysis work by combining Python and SQL in powerful ways; leveraging strengths of respective languages.

Orders of magnitude faster scoring

After demonstrating industry leading 1 million+ rows/sec batch scoring performance, we are now introducing Native Scoring for even faster prediction! Some concurrent prediction scenarios require close to real-time response times. Models trained using the RevoScaleR and revoscalepy packages are supported by this new PREDICT verb (a system table value function) which makes it easy to embed this performant scoring functionality in regular T-SQL SELECT statements without invoking the R or Python runtime. Native scoring is also available on SQL Server on Linux.

Real-time scoring is also available to SQL Server 2016 customers on upgrading in-database R to the latest release of Microsoft Machine Learning Server.

Improving R package management in SQL Server

One of the key values of R is its vibrant community with thousands of open source packages. We have further improved the R package management in SQL Server. We have a rich set of R functions to do package management in SQL Server that gives users the ability to install, uninstall and manage packages in various roles and scopes. In addition, it is now possible to install R packages on SQL Server using TSQL commands (CREATE EXTERNAL LIBRARY). This approach ensures availability of the previously installed packages when a server fails over.

Machine Learning Server

Along with the general availability of SQL Server 2017, we have also announced the general availability of the new Microsoft Machine Learning Server! This is the underlying software that is integrated into SQL Server as Machine Learning Services. Machine Learning Server is the transformation of Microsoft R Server into an even more flexible platform that offers a choice of R and Python languages and brings the best of algorithmic innovations from the open source world and Microsoft. Its multi-platform support enables customers to build portable models wherever their data is and operationalize the models on platforms like SQL Server, making the intelligence easily consumable by business applications.

Key new algorithmic innovations of Machine Learning Server benefitting the SQL Server scenarios are:

Revoscalepy

This package has the Pythonic version of Microsoft’s proprietary Parallel External Memory Algorithms (APIs for linear and logistic regressions, decision tree, boosted tree and random forest) and a rich set of APIs for ETL, remote compute contexts and data sources. These are the same scalable and parallelized algorithms (with ‘rx’ prefix) that have been the differentiating value proposition of Microsoft R Server and allow scaling analytics to arbitrarily large datasets, way beyond the available memory.

microsoftml

This package is a set of state of the art, battle-tested ML algorithms and transforms with Python bindings including deep neural net, one class SVM, fast tree, forest, linear and logistic regressions etc. In addition, this package contains pre-trained models for extracting features from images using ResNet models, and doing sentiment analysis from English language text, which dramatically simplifies the creation and deployment of complex AI scenarios on image and text data.

We have also simplified the pricing model to make it easier to acquire and use Machine learning Server on Hadoop. Each SQL Server EE core under Software Assurance gives you rights to use Machine Learning Server on 5 nodes of Hadoop.

Call to action

SQL Server Machine Learning Services is available in all editions of SQL Server 2017 on Windows and we encourage you to download and explore the above-mentioned enhancements in the free express or developer editions. Our R and Python getting started tutorials will walk you through building your first machine learning solutions with SQL Server. You can find additional tutorials on Microsoft docs.

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Expert data scientists are adopting Advanced Analytics (AA) and Machine Learning (ML) at a rapid pace. This pace can be significantly increased when enterprise-grade AA and ML are available within environments where the customers’ data is, infusing intelligence into mission-critical applications is made much easier and, enterprises can turn to a single vendor to make the world of AA and ML synthesized and supported with the SLAs they have come to expect. At Microsoft, our mission has been to make this vision of ambient intelligence a reality for our customers. We took the first step with Microsoft R Server 9.0, and this follow on release includes significant innovations such as:

• New machine learning enhancements and inclusion of pre-trained cognitive models such as sentiment analysis & image featurizers
• SQL Server Machine Learning Services with integrated Python in Preview
• Enterprise grade operationalization with real-time scoring and dynamic scaling of VMs
• Deep customer & ISV partnerships to deliver the right solutions to customers
• A panoply of sources to help you get started with ease

You can immediately download Microsoft R Server 9.1 from MSDN and Visual Studio Dev Essentials. It comes packed with tons of value built on top of the latest open source R engine that makes R enterprise-class. Also check out R Client for Windows and R Client for Linux.

State of the Art Machine Learning

Bring Machine Learning to where your data is

With Microsoft R Server 9.0 release, we provided Machine Learning algorithms battle-tested by Microsoft as MicrosoftML package, available as a part of SQL Server R Services and Microsoft R Server  9.0 on Windows. We have now made these MicrosoftML algorithms portable and distributed to run on Linux, Windows, and the most popular distributions of Hadoop — Cloudera, Hortonworks, MapR, in addition to SQL Server 2016: Fast linear with L1 and L2 regularization, Fast boosted decision tree, Fast random forest, Logistic regression, with support for L1 and L2 regularization, GPU-accelerated Deep Neural Networks (DNNs) with convolutions, Binary classification using a One-Class Support Vector Machine. This blog demonstrates the use of Microsoft ML algorithms on Hadoop and Spark.

Pre-trained Cognitive Models

We make it easy for enterprises to infuse intelligence into their Line of Business (LOB) applications. Conventional methods require significant investments of time and effort to hand-craft Machine Learning models from scratch. Harnessing decades of work on cognitive computing in the context of Bing, Office 365 and Xbox, we are delivering the first installment of pre-trained cognitive models that accelerate time to value. Further, these models can be re-trained with your data and optimized for your business.

We now offer a Sentiment Analysis pre-trained cognitive model, using which you can assess the sentiment of an English sentence/paragraph with just a few lines of code. With the Image Featurizer pre-trained cognitive model, you can derive up to 5,000 features on a given image, and use that to compare similarity between two images. This blog shows you how to benefit from the power of image featurizers and more details of Sentiment Analysis are covered in this blog.

Combining the best of Microsoft Innovation and Open Source

We are delivering on the promise of embracing the best of open source, and pairing it with the best of Microsoft innovation. With this release, within the same R script, you can mix and match functions from RevoScaleR and Microsoft ML packages with popular open source packages like SparklyR and through it, H2O. Refer to this blog for examples on how to get the best of both worlds!

Optimized Algorithm for Pleasingly Parallel

One of the most popular advanced analytics use cases is Pleasingly Parallel where you run massively parallel computations on partitions that are grouped by one or more attributes.  These embarrassingly parallel use cases are common across industries:

• Life sciences simulations to identify the best drug for a given situation
• Portfolio analysis to identify the right investment for each portfolio
• Utilities to forecast energy consumption for each cohort
• Shipping to forecast demand for various container types

We have generalized the pattern and provided a highly performant, simple, and flexible RxExecBy() function within RevoScaleR, to address these use cases. Furthermore, this function is portable across all platforms that support Microsoft R Server — Windows, Linux, Hadoop, SQL Server. More details on how to choose the best algorithm for Pleasingly Parallel use-cases are available here.

This release also includes support for Optimized Row Columnar (ORC) file format which provides a highly efficient way to store Hive data, and distributed merge for Spark compute context, RxMerge().

Enterprise-Grade Operationalization

We recognize that easy, secure, and high-performance operationalization is essential for Tier-1 enterprises, at scale, to derive maximum value from their analytics investments. Microsoft R Server 9.1 release continues strengthening the power of operationalization. See this blog for more details.

• Real time web services: realize 10X to 100X boost in scoring performance, scoring speeds at <10ms. Currently on Windows platform; other platforms will be supported soon.
• Role Based Access Control: enables admins to control who can publish, update, delete or consume web services
• Asynchronous batch processing: speed up the scoring performance for the web services with large input data sets and long-running jobs
• Asynchronous remote execution: run scripts in background mode on a remote server, without having to wait for the job to complete
• Dynamic scaling of operationalization grid with Azure VMs: easily spin up a set of R Server VMs in Azure, configure them as a grid for operationalization, and scale it up and down based on CPU / Memory usage

SQL Server R Services

The innovations in Microsoft R Server 9.1 are available to SQL Server 2016 customers; an easy upgrade of R services in SQL Server 2016 as described in this doc and in this blog post, is all you need. The machine learning and pleasingly parallel enhancements listed in the previous section are fully supported on SQL server as well. SQL Server is the first database in the world that has in-database Machine Learning!

Real-time scoring

With R Services in SQL Server 2016, we set the industry benchmark for high throughput scoring at 1 Million predictions per second. Now, we have improved single row scoring performance significantly, up to two orders of magnitude better than earlier versions. Real-time scoring is supported on models trained using both RevoScaleR and MicrosoftML algorithms & transforms. With this release, SQL Server understands these models natively and scores inputs without the need of R interpreter and associated overhead, delivering significantly better performance.

Flexible R package management

In 9.0.1 release of Microsoft R Server we added functionality in RevoScaleR package that enables users to install, uninstall and manage packages on SQL Server without requiring administrative access to the SQL Server machine. Data scientists and other non-admin users can install packages in specific databases, user or group scope. In this release, we have added the rxSyncPackages API to ensure that the user-installed packages are not lost if the SQL Server node goes down or if the database is migrated. The list of packages and the permissions is maintained in a server table and this API ensures that the required packages are installed on the file system.

SQL Server Machine Learning Services – Python Preview

SQL Server 2016 brought you in-database analytics with SQL Server R Services. With CTP 1 of SQL Server 2017, MicrosoftML provided in-database Machine Learning.  CTP 2.0 of SQL Server 2017 brings you SQL Server Machine Learning Services that embraces both R and Python. Data Scientists can now choose from a huge collection of analytics and machine learning algorithms across R and Python communities to execute in-database and get their job done much more effectively. CTP 2.0 enables collaboration between traditional data scientists with strong R backgrounds and computer scientists with strong Python backgrounds, to deliver the best business ROI.

Additionally, the real-time scoring and flexible package management functionality listed above for SQL Server R Services is also available in the CTP2 release as part of Machine Learning services. Refer to this blog on how to get the best of both R and Python worlds!

Customer & ISV Partnerships

Engaging with Customers

“Working with Microsoft R Server for our data science needs at eToro has been a key factor in our success. The tools are appropriate for all levels of data scientist skills from beginners to seasoned professionals. Using Microsoft R Server, we were able to quickly run large scale statistical simulations in a distributed manner that ensured the robustness of our machine learning models. This partnership was instrumental in meeting our business goals and we look forward to using the continuing innovation coming out on Microsoft R Server!” — Moti Goldklang, Director of Trading Systems, eToro.

We are committed to finding more ways for our customers to connect with us, to understand how to get the most out of their investments and provide feedback to influence product direction.  We offer a variety of ways customers can engage closely with Microsoft and provide product feedback.

R Advisors: We launched R Advisors where we work with leading customers to shape our future products. Azure Advisors is an opt-in program that you can sign up on behalf of your organization.

User Voice: As a customer focus team, we are interested in listening to your feedback and to help us steer our product capability we are launching User Voice for Microsoft R today. You can partake in discussion and cast your vote on features that you’d like to see us enable. We are listening!

Authoring Tools from Microsoft and Partners

I am happy to announce that we have a number options to help you develop Microsoft R based applications, both from Microsoft and from our partners. R Tools for Visual Studio (RTVS) is now Generally Available, and brings support for Microsoft R into Visual Studio. In addition, we also have Python Tools for Visual Studio (PTVS) for your Python development. In addition, we have worked with MicroStrategy, Alteryx and KNIME, and, augmented open source Rattle, to give you more choices.

Microsoft has been contributing to the R Community to ensure that there is an open source WYSIWYG tool to do big data analytics in the community. We have enhanced the popular Rattle package to support Microsoft R Server capabilities. You can download the latest, and stay abreast with the developments here.

With Alteryx Designer 11.0, a self-service analytics workflow tool from Alteryx, business analysts and data scientists can work with Microsoft R Server and SQL Server R Services. In the words of Neil Ryan, Product Manager at Alteryx, “At Alteryx we’re acutely aware of the challenge of getting faster insights from very large datasets. When it comes to computation-intensive machine learning, it’s even more important to leverage existing hardware resources and keep the processing as close as possible to where the data lives. That’s why we’re excited about our partnership with Microsoft. By leveraging Microsoft R Server and SQL Server’s in database analytics, our customers are scaling their analytics to the size of their data through a consistent, code-free, drag and drop interface for both data preparation and modeling within SQL Server.” More details are in this blog post.

Microsoft and KNIME have partnered to bring Microsoft R capabilities to the KNIME platform. “KNIME has added the option to reach out to Microsoft R from KNIME Analytics Platform to make a scalable and enterprise ready R integration part of any KNIME workflow,” says Michael Berthold, CEO of KNIME. Here is an example of how this works and you can see it in action here.

MicroStrategy has made Microsoft R runtime accessible from MicroStrategy Desktop. “MicroStrategy is embracing Microsoft R in our analytics platform tools to bring the power of advanced analytics and machine learning to our customers. We just announced this at MicroStrategy World and you can read more about this here,” says Sandipto Banerjee, VP Data Group & Advanced Technologies, MicroStrategy.

Getting Started

The best place to get started is our comprehensive documentation site, which introduces concepts, platforms, features, code samples, and how-tos. Our vibrant blogs include R Server Blog that was  launched earlier this year on all thing R, R Tiger Team which covers deep technical insights on Microsoft R Server, and the Revolutions R Blog which highlights both Microsoft R and open-source R innovations. Together, these blogs provide a plethora of articles, tips and tricks for novices and experts alike. I welcome you to check these out and leave us your comments.

Check out the free Data Science with Microsoft SQL Server 2016 eBook that covers what’s new, installing & configuring R Services, and how to develop full applications through walkthroughs.

Want to get certified and show your mastery in data science? We have your covered via several courses at Microsoft LearnAnalytics and Microsoft Academy! We have several training partners that can help you train your teams on advanced analytics and machine learning!

Solution Templates

Check out the R Solution Templates that will walk you through how to develop a solution using Microsoft R Server, from beginning to end. In addition, with the click of a button, you can deploy these templates to an AzureVM and see the entire application in action. You can follow the links to github and use the code as a starting point for your own solution, and accelerate time to value!

In our last release, we provided a Solutions Template for Campaign Optimization using SQL Server R Services.  Now, we have added a solution template for the Azure HDInsight platform on Spark compute context.  In the words of Anindya Palit, EVP Affine Analytics, “Partnering with Microsoft allowed Affine’s extensive analytics experience in marketing to be transformed into a solution for optimizing lead generation through Campaign Optimization. We were able to quickly ramp up and build the solution utilizing the power of R Services within SQL Server.”

Hospital Length Of Stay (LOS) is the latest solution template built on SQL Server R Services. Dr. Greg Mckelvey, Head of Clinical Insights, KenSci, says “The Hospital ‘Length of Stay Prediction’ solution shows how you can build a potentially life-saving machine learning solution by leveraging the power of R within SQL Server. By predicting how long an admitted patient is likely to stay at the hospital based on clinical history, labs and vital, the solution enables doctors and nurses to better manage patient flow and coordinate post-discharge patient care.”

Azure VMs

Microsoft R Server 9.1 will be released as Azure VMs in Azure Marketplace, Data Science VMs, and on Azure HDInsight. VMs were available on CentOS 7.2 and Ubuntu 16.04. Now, we have added support for RHEL 7.2, and made all VMs available in China.

Microsoft R Client

With our current release, we are delivering Microsoft R Client on the Linux platform for the first time, in addition to Windows. R Client is available on all four popular flavors of Linux – RHEL, CentOS, Ubuntu, and SuSE.  Please check out R Client for Windows and R Client for Linux.

In Summary

I am proud of how we are making R enterprise-grade through Microsoft R portfolio of products and services, building on top of open source R in fully compatible ways. Adopting advanced analytics and machine learning requires a holistic approach that transcends technology, people and processes; we continue to deliver more handholding to ensure that enterprise users are set up for success! With the 9.1 release, you have in-database analytics and machine learning in a variety of platforms, develop powerful analytics models leveraging both open source and Microsoft innovation, deploy them at scale, and easily integrate into line-of-business systems to maximize ROI.

Nagesh Pabbisetty

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