Microsoft Machine Learning Server Reviews & Pricing

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. 

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