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Benefits and Insights

Why use Microsoft Machine Learning Server?

Key differentiators & advantages of Microsoft Machine Learning Server

  • Workflow Construction Flexibility: Users can code in either Python or R based on their knowledge or needs. The solution has robust support and extensions for both languages. 
  • Machine Learning and AI: Microsoft’s industry-leading AI and machine learning automate much of the development process and increase the final product’s accuracy. The analyses get smarter and more accurate the more data they accrue. 
  • Turn Insights Into Actions: With built-in operationalization and web service publishing features and cloud-based deployment, new models and insights can be utilized immediately. It can implement services into client applications without format conversion. 
  • Prebuilt in Microsoft Azure: Microsoft Azure, the company’s virtual machine branch, features prebuilt images of the Machine Learning Server, enabling model training, execution and testing in an isolated, secure environment.  
  • Predictive Analytics: Its framework for distributed computing, support for the two premier predictive data analytics programming languages, integrations and AI/machine learning capabilities combine to produce quick and accurate projections and models. 
  • Highly Scalable: Cloud deployment, remote session launching and integration with distributed computing platforms make it capable of big data analytics as needed.  

Industry Expertise

Microsoft Machine Learning Server began life as Revolution R Enterprise, a product of Revolution Analytics. In 2010, it introduced RevoScaleR, a multi-core processing framework which set a new industry standard for big data analytics speed in R. The company was acquired by Microsoft in 2015, which added its vast resources and expertise in machine learning and AI to the product to keep it in among the industry leaders in big data and enterprise intelligence.

Key Features

  • Python and R Support: The solution can cater to both Python and R users. It has built-in interpreters and extensive function libraries for both languages and support for tools like RStudio to implement them. 
  • Analytics: It can process data at industry-leading speeds, using distributed computing and parallel threading. Using Microsoft’s proprietary packages RevoScaleR and revoscalepy, it processes only relevant data on predictive analyses, reducing the amount of information used and increasing speed. 
  • Operationalization: Users can implement insights sooner, utilizing its built-in capabilities to export models and code for easy consumption within client applications. It processes these models into “web services,” which can then be deployed to applications and backend systems. 
  • Remote Code Execution: It allows for remote sessions to process data on different servers and test models, which can further disperse workload on a model. The instanced work can be published as a web service. 
  • Machine Learning: It automatically updates models with larger datasets, making projections and predictions more accurate as more information accrues. These updates are applied through its operationalization features. 
  • Pretrained Models: For users who don’t have access to datasets to train new models, it offers pre-trained models for unstructured data processing. These models are capable of sentiment analysis and image detection.  
  • Variable Deployment: Depending on scale, speed, access and storage needs of the user, the system can be deployed on-premises, in the cloud or hybrid of both. 
  • Utilize Extensive Integrations: Integrations with open-source platforms like Hadoop and Apache Spark enable access to more relational data and processing clusters for smarter, faster modeling. 
  • Big Data Analytics: Users can query and process vast scales of data through distributed computing and function libraries like RevoScaleR, revoscalepy and olapR. 

Microsoft Machine Learning Server Suite Support

The vendor offers 24/7 support to customers, with much of the support access information hidden behind a customer login portal. It also offers a user forum and documentation through its website.

mail_outlineEmail: Email support information is hidden behind the customer login wall.
phonePhone: No phone support information is available.
schoolTraining: The vendor offers data science essentials and programming with R for data science courses through its website. Additional training information is hidden behind the customer login wall.
local_offerTickets: Support ticket information is hidden behind the customer login wall.
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