This post is authored by Nagesh Pabbisetty, Partner Director of Program Management at Microsoft.
Earlier this year, Microsoft CEO Satya Nadella shared his vision for Microsoft and AI, pointing to Microsoft’s beginnings as a tools company, and our current focus on democratizing AI by putting tools “in the hands of every developer, every organization, every public sector organization around the world”, so that they can build their own intelligence and AI capabilities.
Today, we are taking a significant step in realizing Satya’s vision by launching Microsoft Machine Learning Server 9.2, our most comprehensive machine learning and advanced analytics platform for enterprises. We have exciting updates to share, including full data science lifecycle support (data preparation, modeling and operationalization) for Python as a peer to R, and a repertoire of high performance distributed ML and advanced analytics algorithm packages.
We started the journey of transforming Microsoft R Server into Machine Learning Server a year ago, by delivering innovations in Microsoft R Server 9.0 and 9.1. We made significant enhancements in this release to create the Machine Learning Server 9.2 platform which replaces Microsoft R Server and offers powerful ML capabilities.
Microsoft Machine Learning Server is the most inclusive enterprise platform that caters to the needs of all constituents – data engineers, data scientists, line-of-business programmers and IT professionals – with full support for Python and R. This flexible platform offers a choice of languages and features algorithmic innovation that brings the best of open source and proprietary worlds together. It enables best-in-class operationalization support for batch and real-time.
Microsoft Machine Learning Server includes:
- High-performance ML and AI wherever your data lives.
- The best AI innovation from Microsoft and open source.
- Simple, secure and high-scale operationalization and administration.
- A collaborative data science environment for intelligent application development.
- Deep ecosystem engagements, to deliver customer success with optimal TCO.
It’s now easier than ever to procure and use Microsoft Machine Learning Server on all platforms. Licensing has been simplified to the following, effective October 1st 2017:
- Microsoft Machine Learning Server is built into SQL Server 2017 at no additional charge.
- Microsoft Machine Learning Server stand-alone for Linux or Windows is licensed core-for-core as SQL Server 2017.
- All customers who have purchased Software Assurance for SQL Server Enterprise Edition are entitled to use 5 nodes of Microsoft Machine Learning Server for Hadoop/Spark for each core of SQL Server 2017 Enterprise Edition under SA. In addition, we are removing the core limit per-node; customers can have unlimited cores per node of Machine Learning Server for Hadoop/Spark.
You can immediately download Microsoft Machine Learning Server 9.2 from MSDN. It comes packed with the power of the open source R and Python engines, making both R and Python ready for enterprise-class ML and advanced analytics. Also check out the R Client for Windows, R Client for Linux, and Visual Studio 2017 with R and Python Tools.
Let’s take a peek at each of the key areas of the new Microsoft Machine Learning Server outlined above.
1. High-performance Machine Learning and AI, Wherever Data Lives
The volume of the data that’s being used by enterprises to make smart business decisions is growing exponentially. The traditional paradigm requires users to move data to compute which introduces challenges with latency, governance and cost, even if it was possible to move the data to where compute is. The modern paradigm is to take compute to where the data is, to unlock intelligence, and this is Microsoft’s approach.
In enterprises, it is common to have data spread across multiple data platforms and migrate data from one platform to another, over time. In such a world, it is essential that ML and analytics are available on multiple platforms, and are portable, and Microsoft delivers on this need. Microsoft Machine Learning Server 9.2 runs on Windows, three flavors of Linux, the most popular distributions of Hadoop Spark and in the latest release of SQL Server 2017. As always, we will soon make this release available on Azure as Machine Learning Server VMs, SQL Server VMs, and as Machine Learning Services on Azure HDInsight, in addition to an ever-growing portfolio of cloud services.
Today, we are also announcing Public Preview of R Services on Azure SQL DB, to make it easy for customers who are going cloud-first or transitioning to the cloud from on-premises.
For more information, review the links below:
- Supported platforms and versions.
- SQL Server Machine Learning Services.
- Microsoft R Server for Spark on Azure HDInsight.
- R Server for Hadoop.
- Machine Learning Server for Hadoop.
2. The Best AI Innovation from Microsoft and Open Source
As make AI accessible to every individual and organization, one of our key goals is to use this technology to amplify human ingenuity through intelligent technology. We are designing AI innovations that extend and empower human capabilities in all aspects of life. We are infusing AI across our most popular products and services, and creating new ways to interact more naturally with technology. Offerings such as the Microsoft Cognitive Toolkit for deep learning, our Cognitive Services collection of intelligent APIs, SQL Server Machine Learning Services and Azure Machine Learning exemplify our approach.
Microsoft Machine Learning Server includes a rich set of highly scalable and distributed set of 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.
Microsoft Machine Learning Server 9.2 bridges these two worlds, enabling enterprises to build on a single ML platform where one can bring any R or Python open source ML package, and have it work side-by-side with any proprietary innovation from Microsoft. This is a key investment area for us. You can learn more from the resources below:
- Deep Learning at Cloud Scale with Microsoft Machine Learning Server.
- Predictive Analytics with Microsoft Machine Learning Server.
- How to Choose a MicrosoftML Algorithm.
- Running Pleasingly Parallel workloads on Spark, SQL Server and Local Compute Contexts.
- Training an ensemble of models.
- Interoperability with Sparklyr and RevoScaleR.
- Interoperability with PySpark and RevoScalePy.
3. Simple, Secure and High-Scale Operationalization and Administration
Enterprises that rely on traditional paradigms and environments for operationalization end up investing a lot of time and effort towards this area. It is not uncommon for data scientists to complete their models and hand them over to line-of-business programmers to translate that into popular LOB languages and APIs. The translation time for the model, iterations to keep it valid and current, regulatory approval, managing permissions through operationalization – all of these things are big pain points, and they result in inflated costs and delays.
Microsoft Machine Learning Server offers the best-in-class operationalization solution in the industry. From the time an ML model is completed, it takes just a few clicks to generate web services APIs that can be hosted on a server grid (either on premises or in the cloud) which can then be integrated with LOB applications easily. In addition, Microsoft Machine Learning Server integrates seamlessly with Active Directory and Azure Active Directory, and includes role-based access control to make sure that the security and compliance needs of the enterprise are satisfied. 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.
For more information, refer to the links below:
- Machine Learning Server operationalization video and deck.
- Deploy a Python model as a web service.
- Deploy an R model as a web service.
- Scale up with 1 click.
- Real-time scoring, batch scoring.
- 1 million scores per second.
4. A Collaborative Data Science Environment for Intelligent Application Development
In enterprises, different departments take the lead for different aspects of the data science life-cycle. For instance, data engineers lead data preparation, data scientists lead experimentation and model building, IT professionals lead deployment and operationalization, and LOB programmers develop and enhance applications with intelligence, tailoring them to the needs of the business. With the in-database analytics capability of SQL Server 2017 and SQL Server 2016 (powered by Microsoft Machine Learning Services), all these constituents can work collaboratively and in the context of the leading mission critical database that is trusted by enterprises all over the world.
Python and R are the most popular languages for ML and advanced analytics. The choice of a language depends on the expertise and culture of engineers and scientists, the data science problems to be solved, and the availability of algorithms toolkits for the chosen language. Each language is supported by a choice of open-source IDEs. It’s not unusual to have debates on which language to choose because enterprises think they have to make an either-or choice.
With Microsoft Machine Learning Server, both R and Python are fully supported. You can bring in and use the latest open source toolkits along with the included Microsoft toolkits for AI and advanced analytics, all on top of a single enterprise-grade platform. Specific enhancements to support Python in the current release include:
- New Python packages: revoscalepy and microsoftml, bringing high performance and battle tested machine learning algorithms to Python users.
- Pre-trained cognitive models for image classification and sentiment analysis.
- Interoperability with PySpark.
- Python models deployed as web services.
- Real-time and batch scoring of Python models.
Concurrent with this release, Microsoft is also releasing a public preview of Azure Machine Learning, a comprehensive environment for data science and AI. We will integrate Microsoft Machine Learning Server capabilities with this platform, to realize an industry-leading workbench for data science and AI.
For more information, refer to the links below:
- Azure Machine Learning Public Preview.
- SQL Server Machine Learning Services with Python.
- Data Science Tools in Visual Studio 2017.
- Continuum partnership.
- What is Microsoft R?
- Microsoft R Archive Network.
5. Deep Ecosystem Engagements, to Deliver Customer Success with Optimal TCO
Individuals embarking on the journey of making their applications intelligent, or, simply wanting to learn the new world of AI and ML, need the right learning resources to help them get started. Microsoft provides several learning resources, and has engaged several training partners to create a repertoire of solution templates to help you ramp up and become productive quickly, including the following:
- Microsoft Learn Analytics.
- Microsoft Machine Learning Server documentation.
- Data Science with Microsoft SQL Server 2016.
- Marketing Campaign Optimization on SQL Server
R Server for HDI Spark Cluster.
- Predicting Hospital Length of Stay on SQL Server.
- Predicting Loan Credit Risk on SQL Server on R Server for HDI Spark Cluster.
- Loan Charge-off Prediction on SQL Server on R Server for HDI Spark Cluster.
- Fraud Detection for Online Retailers on SQL Server on R Server for HDI Spark Cluster.
Enterprises have big investments in infrastructure and applications and may need the help of partners such as Systems Integrators (SIs) and Independent Software Vendors (ISVs) to help them transform into the world of intelligent applications. Microsoft has nurtured a vibrant ecosystem of partners to help our customers here. Learn about some of our strategic partnerships at the links below:
- Leveraging Microsoft R and in-database analytics of SQL Server with R Services through Alteryx Designer.
- Alteryx and Microsoft R Server Demo.
- Knime Analytics Platform with SQL Server and HDInsight.
- Machine Learning and Analytics with Microsoft R with Microstrategy.
With the launch of Microsoft Machine Learning Server 9.2, we are proud to bring enterprises worldwide an inclusive platform for machine learning and advanced analytics. We have created a better-together environment that brings intelligence where the data lives, supports both R and Python, both open source and proprietary innovation, the ability to work on the data science lifecycle across a wide variety of platforms, and infuse intelligence at scale, both in batch and real-time contexts, with APIs for the most popular LOB languages.
Adopting machine learning and advanced analytics requires a holistic approach that transcends technology, people and processes. We are proud to continue delivering the best tools, platforms and ecosystem to ensure that enterprise users are set up for success. Our next steps are to integrate Azure Machine Learning and Microsoft Machine Learning Server closely, and continue to take machine learning to our customers’ data, wherever it may reside.