SAS Visual Machine Learning: Product Overview and Insight

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Bottom Line

SAS Visual Data Mining and Machine Learning delivers an integrated platform for managing enterprise data requirements and developing machine learning models. The highly scalable environment supports concurrent access to data across multiple users and groups. As such, it is clearly designed for large enterprise buyers with demanding ML tasks in front of them. On the the other hand, its visual interface needs no coding – so it offers cost savings on developers.

SAS Visual Data Mining and Machine Learning is a truly multimodal PAML (predictive analytics and machine learning) solution. It includes a wizard that guides users through the process of training a model, converting it to editable code, and then converting it back to a visual pipeline.  The solution distributes data and analytical workload operations across nodes, in parallel, and are multithreaded on each node so that processes take place at ultra-fast speeds.

SAS Visual Data Mining and Machine Learning offers a graphical drag-and-drop interface, integrated text analytics, pre-defined algorithms, and tools to test different modeling approaches in a single run. The platform provides a rich set of APIs for managing coding and powerful tools for viewing data and models, refining them and putting them to use. SAS Visual Data Mining and Machine Learning earned top ratings from both Gartner and Forrester.

Product Description

The platform offers interactive programming in a web-based development environment. A visual interface spans the entire analytics and machine learning framework. It includes a drag-and-drop interface that requires no coding (though coding is an option).

The package includes best practice templates and the ability to explore data and results from within SAS Model Studio (an application for developing machine learning pipelines in SAS Visual Data Mining and Machine Learning) and SAS Visual Analytics. The platform features distributed, in-memory processing of complex analytical calculations on large data sets with concurrent access to the same data in memory. It also supports neural networks and supports multiple machine learning methods, including deep neural networks (DNNs), convolutional neural networks (CNNs), recurrent neural networks (RNNs) and autoencoders.

All of this provides a framework for both supervised and unsupervised learning and supports open source tools, including Python, R and TensorFlow. The platform is available both on-site and in the cloud. It accommodates text analytics for 32 languages out of the box.

Overview and Features

User Base

Data scientists, statisticians, data miners, business analysts, citizen data scientists, data engineers and researchers.

Interface

Graphic user interface with drag-and-drop. Also supports open source scripting tools.

Scripting Languages/Formats Supported

Python, R, Java, Lua, Scala and others.

Formats Supported

Data residing in SAS Visual Analytics. It can import numerous file and format types, including CSV and XLS.

Integration

SAS Viya REST APIs connects data and models to other applications. The platform supports Cloud Foundry as well as public cloud storage through Amazon Web Services and Microsoft Azure.

Reporting and Visualization

Rich visualization features through SAS Visual Analytics and other tools. Offers interpretability reports.

Pricing

N/A

SAS Visual ML Overview and Features at a Glance:

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