Flexible, approachable visual environment for analytics
Lets multiple users concurrently analyze any amount of structured and unstructured data with an easy-to-use visual interface.
Supports the end-to-end data mining and machine-learning process with a comprehensive visual – and programming – interface that handles all tasks in the analytical life cycle.
Highly scalable in-memory analytical processing
Enables concurrent access to data in memory in a secure, multiuser environment. Data and analytical workload operations are distributed across nodes, in parallel, and are multithreaded on each node for very fast speed.
Integrated data preparation, exploration and feature engineering
Drag-and-drop interface lets data engineers quickly build and run transformations, augment data and join data within the integrated visual pipeline of activities. All actions are performed in memory to maintain data structure consistency.
Integrated text analytics
Lets you explore all textual data, not just a subset, to gain new insights about unknown themes and connections.
Innovative statistical, data mining and machine learning techniques
Provides access to an incredibly broad set of modern statistical, machine learning, deep learning and text analytics algorithms in a single environment. Analytical capabilities include clustering, different flavors of regression, random forests, gradient boosting models, support vector machines, natural language processing, topic detection and more.
Model assessment and scoring
Test different modeling approaches in a single run and compare results of multiple supervised learning algorithms with standardized tests to quickly identify champion models. Then operationalize analytics in distributed and traditional environments with automatically generated SAS score code.
Accessible and cloud-ready
Modelers and data scientists can access SAS capabilities from their preferred coding environment, whether it's Python, R, Java or Lua. And with SAS Viya REST APIs, you can add the power of SAS to other applications.
From data management to model development and deployment, everyone works in the same, integrated environment.
Solve complex analytical problems faster.
SAS Visual Data Mining and Machine Learning, which runs on SAS® Viya®, combines data wrangling, exploration, feature engineering and modern statistical, data mining and machine learning techniques in a a single, scalable in-memory processing environment. The solution provides a very visual and highly collaborative workspace that supports a variety of users with different skill sets.
Empower users with language options.
Don't know SAS code? No problem. SAS Visual Data Mining and Machine Learning lets you embed open source code within an analysis, and call open source algorithms seamlessly within a Model Studio flow. This facilitates collaboration across your organization, because users can program in the language of their choice. The new node in Model Studio is agnostic to Python or R software versions; any version can be used as the code is passed.
Explore multiple approaches quickly to find the optimal solution.
Superior performance from massive parallel processing and the feature-rich building blocks for machine-learning pipelines let you explore and compare multiple approaches rapidly. You can quickly and easily find the optimal parameter settings for diverse machine learning algorithms – including decision trees, random forests, gradient boosting, neural networks, support vector machines and factorization machines – simply by selecting the option you want. Complex local search optimization routines work hard in the background to efficiently and effectively tune your models. The solution also lets you combine unstructured and structured data in integrated machine learning programs for more valuable insights from new data types. And reproducibility in every stage of the analytical life cycle delivers answers and insights you can trust.
Boost the productivity of your analytical teams.
Data scientists and other analytical professionals can get highly accurate results from a single, collaborative environment that supports the entire machine-learning pipeline. The solution enables a variety of users to access and prepare data. Perform exploratory analysis. Build and compare machine learning models. Create score code for implementing predictive models. Execute one-click model deployment. And you can do all this faster than ever before.
Reduce latency between data and decisions.
To enhance collaborative understanding, the solution provides all users with business-friendly annotations within each node describing what methods are being run, as well as information about the methods, results and interpretation. Standard interpretability reports are also available in all modeling nodes, including LIME, ICE, PD plots, etc.
Get to Know SAS® Visual Data Mining and Machine Learning
This solution runs on SAS® Viya®, which has the breadth and depth to conquer any analytics challenge, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster.
Explore More on SAS® Visual Data Mining and Machine Learning & Beyond
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Customer Story
Shaping higher education through cloud analyticsGeorge Mason University relies on SAS Viya to uncover insights to improve operational effectiveness and increase student success.
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Article
Machine learning and artificial intelligence in a brave new worldWhat is the interplay between man and machine in a brave new world with AI?
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White Paper
How to Do Deep Learning With SAS® Deep learning is a type of machine learning that trains a computer to perform humanlike tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper provides an introduction to deep learning techniques, applications and how SAS supports the creation of deep neural network models. It is geared towards a data scientist and includes step-by-step overview of how to build a deep learning model using SAS.
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Customer Story
Finding the next football star with artificial intelligenceDutch sports analytics company SciSports uses emerging tech to innovate on the pitch.
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Customer Story
Shaping higher education through cloud analyticsGeorge Mason University relies on SAS Viya to uncover insights to improve operational effectiveness and increase student success.
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Customer Story
Finding the next football star with artificial intelligenceDutch sports analytics company SciSports uses emerging tech to innovate on the pitch.
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Customer Story
Zeroing in on property values with machine learningArtificial intelligence improves assessment accuracy and productivity in Wake County.
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Customer Story
Machine learning and cloud analytics lead to better bankingATB Financial uses artificial intelligence and SAS Viya on Google Cloud to halve processing times and enhance the customer experience.
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Interview
How does one of the largest cities in the world use data for social good? From data privacy to data quality, what are the challenges in using data for social good, and how does one large organization in New York City address them? Hear from a research scientist at the Center for Innovation through Data Intelligence about the data they have, the questions they ask of it, and the data they’d like to see in the future.
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Article
5 Challenges for IoT in the insurance industryIoT promises to substantially reduce losses in the insurance industry, but adoption is low. That will change when the industry overcomes these five challenges.
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Article
Supporting indigenous communities with analyticsThough indigenous women are a small part of Canada's population, they account for a disproportionately large number of Canada's murder victims. Now, big data and analytics are being used to help improve outcomes.
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Article
Five AI TechnologiesDo you know the difference between artificial intelligence and machine learning? And can you explain why computer vision is an AI technology? Find out in this short explainer.
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Research
Nerd in the herd: protecting elephants with data scienceA passionate SAS data scientist uses machine learning to detect tuberculosis in elephants. Find out how her research can help prevent the spread of the disease.
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Article
Edge computingWith traditional methods, data is captured, stored and analyzed later – limiting how quickly businesses can act on insights from the data. With edge computing, IoT data is processed at the edge of a network – right where it’s created or collected – avoiding delays and enabling real-time processing and action.
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Article
IoT challenges in the connected supply chainThe IoT Is set to revolutionize supply chain management, but there are certain challenges it must overcome first. Here’s what you need to know.
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IT/OT convergence: The dilemma of the IoT perception gapThe IoT phenomenon demands intimate collaboration between information technology (IT) and operational technology (OT). But IT and OT have distinct gaps in perception about the goals and outcomes of IoT initiatives. Tom Bradicich explains why IT/OT convergence is essential for successful IoT projects.
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Check out these products related to SAS Visual Data Mining and Machine Learning, built on the powerful SAS® Platform.
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SAS® Enterprise Miner™Streamline the data mining process to create highly accurate predictive and descriptive models based on large volumes of data.
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SAS® Visual StatisticsCreate and modify predictive models faster than ever using a visual interface and in-memory processing.
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SAS® ViyaConquer your analytics challenges, from experimental to mission critical. SAS Viya extends the SAS Platform to enable everyone – data scientists, business analysts, developers and executives alike – to collaborate and realize innovative results faster.
