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.
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.
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.
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.
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.
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.
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