In September 2017, Microsoft R Server got a new name to reflect changes in its design and support capacity. Going by the name Microsoft Machine Learning Server, it now supports Python Language. Furthermore, it enhanced the already existing R capabilities and incorporated other improvements.
In this post, you will get an in-depth look into what Microsoft Machine Learning Server is, the benefits of using it and its key features.
What is the Microsoft Machine Learning Server?
Microsoft Machine Learning Server is a flexible platform for enterprises offering full support for Python and R. Its key objectives include the analysis of data at scale, creation of intelligent apps and allowing users to uncover useful business insights on the platform.
Versatility is at the core of its design as it meets the needs of a wide range of professionals. These include data scientists and engineers as well as IT experts and programmers. Another unique aspect of its design is the fact that it merges the two worlds of open-source and proprietary technology, with a wide choice of languages and algorithmic innovation.
As its name implies, the Machine Learning Server has significant AI capabilities. For R support, it uses a legacy of Revolution R Enterprise and Microsoft R Server 9.x products. Python support was added much later than R and the server supports the full lifecycle of data science in Python-based analytics. By virtue of its operationalization support, the platform lets you deploy models to the scalable grid for real-time and batch scoring.
Benefits of Using Microsoft Machine Learning Server
The use of Machine Learning Server presents a key value proposition in the form of delivering advanced analytics at scale using Python and Enterprise R. Furthermore, rather than requiring users to pull data across networks, it brings processing and calculation to where the data lives. As a result, it offers a number of crucial benefits:
- Security and high performance
Businesses have access to more data than ever before to aid informed decision making. Over time, such data will spread to reside across multiple platforms and this poses a challenge on how to bring machine learning to these different locations.
As mentioned above, the Machine Learning Server brings computation to where the data is and unlocks intelligence. Eliminating the need to bring data to the server and instead bringing R and Python execution to the data negates the possibility of insecure data movement and also avoids waste.
- Open-source and innovative
Through the product, Microsoft is taking strides to offer everyone, both at the organizational and individual level, unfettered access to AI. The rich set of distributed algorithms which are highly scalable such as microsoftML, Revoscalepy and RevoscaleR are parts of the server.
Not only can they run in a distributed manner on a variety of platforms, but they can also work on datasets much bigger than their physical memory. The product includes a wide collection of custom Python packages and R Packages from Microsoft.
In order to facilitate convenient use, Microsoft Machine Learning Server bridges its various innovations together with the ones from the open-source community. And all this operates under one enterprise-grade platform.
As it stands, any Microsoft innovation can work seamlessly with any Python or R open-source machine learning package.
Due to database optimization for set-based operations, summaries and aggregations take place at lightning speed. This is largely the result of recent innovations such as in-memory tables which make an ideal complement to data science.
- Simple, high scale operationalization
At the moment, enterprises that rely on legacy operationalization environments suffer a number of noteworthy challenges. Most significantly, they have to deal with delays and exaggerated costs. These are being caused by models translation time, regulatory issues, iterations to maintain validity and keep models current and managing permissions.
But with Machine Learning Server, they get access to top-of-the-range operationalization. As soon as a model is completed, all it requires is a few clicks to create APIs for web services. The system hosts the web services on a server grid which can be on-site or in the cloud and these in turn can integrate with line-of-business applications.
Furthermore, Machine Learning Server integrates with Azure Active Directory and Active Directory. And to satisfy compliance and security needs, it includes role-based access control.
The use of an elastic grid translates to seamless scaling based on the needs of an enterprise and applies both for real-time and batch scoring.
- Easy integration and deployment
The server becomes the central operational point for data management applications and tasks. Using the data present in the reporting warehouse or database ensures that machine learning solutions use up-to-date and consistent data.
- Greater efficiency
Both on-premises and on the cloud, users get access to various enterprise data pipelines rather than processing data on R or Python sessions. Moreover, results analyses and reporting is easy with the use of Reporting Services or Power BI.
- Deep engagements for success
Getting started in the world of AI and machine learning with the aim of learning the ropes or making applications intelligent requires the right set of resources and tools. Microsoft offers plenty of learning resources and training partners for companies and help them reach productivity fast.
Key Features of the Microsoft Machine Learning Server
Support for R and Python are some of the most outstanding features of the Machine Learning Server. Its R and Python function libraries support machine learning and data science on all sizes of datasets. This applies whether they reside on distributed or parallel systems, and offer modern algorithms for data mining, supervised learning and predictive analysis. In addition to these, there are other features worth noting. Take a look:
- Pre-trained Models – The main advantage of using pre-trained models is that you can start scoring and classifying new content immediately. The two main available options are sentiment analysis and image detection models and are available for both Python and R development.
Some users, however, choose to train their own models using their own data, which the Machine Learning Server also supports.
- Remote Execution Capabilities – It is possible to initiate remote sessions from a client workstation on the Machine Learning Server in your network.
- Scale Out on Premises – Thanks to the operationalization capacity built into the product, one can access clustered topologies for Spark on Hadoop as well as Linux or Windows.
- Deployment – You can operationalize the server and use it to deploy solutions in the form of a web service. In essence, a web service is a Python or R code execution carried out on the operationalization compute node.
Getting the most out of the Microsoft Machine Learning Server
There are constant updates being made to the Microsoft Machine Learning Server to enhance performance and efficiency. To benefit from these updates, all you have to do is bind to the product, which will automatically swap out older features and replace them with newer ones. That will not change the fundamentals of the installation in place; rather, setup will simply detect existing features and overwrite them with the new libraries and executables.
For new users, there are tutorials and how-to guides to help you become familiar with the concept and join the machine learning revolution.
- Machine Learning for Content Filtering – Winning the Battle against Harassment and Trolling - January 30, 2020
- 7 Steps to Correct Data Preparation for Machine Learning - January 22, 2020
- Machine Learning Chatbots: What You Should Know about Neural Conversation Agents - December 18, 2019